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Human Gait Recognition for Biometrics Application Based on Deep Learning fusion Assisted Framework

Computers, Materials & Continua 2024年第1期78卷 357-374页

作者： Ch Avais Hanif Muhammad Ali Mughal Muhammad Attique Khan Nouf Abdullah Almujally Taerang Kim Jae-Hyuk Cha Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea

The demand for a non-contact biometric approach for candidate identification has grown over the past ten years.Based on the most important biometric application,human gait analysis is a significant research topic in computer vision.Researchers have paid a lot of attention to gait recognition,specifically the identification of people based on their walking patterns,due to its potential to correctly identify people far away.Gait recognition systems have been used in a variety of applications,including security,medical examinations,identity management,and access control.These systems require a complex combination of technical,operational,and definitional considerations.The employment of gait recognition techniques and technologies has produced a number of beneficial and well-liked applications.Thiswork proposes a novel deep learning-based framework for human gait classification in video sequences.This framework’smain challenge is improving the accuracy of accuracy gait classification under varying conditions,such as carrying a bag and changing clothes.The proposed method’s first step is selecting two pre-trained deep learningmodels and training fromscratch using deep transfer learning.Next,deepmodels have been trained using static hyperparameters;however,the learning rate is calculated using the particle swarmoptimization(PSO)algorithm.Then,the best features are selected from both trained models using the Harris Hawks controlled Sine-Cosine optimization algorithm.This algorithm chooses the best features,combined in a novel correlation-based fusion technique.Finally,the fused best features are categorized using medium,bi-layer,and tri-layered neural networks.On the publicly accessible dataset known as the CASIA-B dataset,the experimental process of the suggested technique was carried out,and an improved accuracy of 94.14% was achieved.The achieved accuracy of the proposed method is improved by the recent state-of-the-art techniques that show the significance of this work.

关键词： Gait recognition covariant factors biometric deep learning fusion feature selection

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Misdiagnosis of synovial sarcoma-cellular myofibroma with SRFRELA gene fusion:A case report

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World Journal of Clinical Cases 2024年第7期12卷 1326-1332页

作者： Ying Zhou Yi-Wen Sun Xiao-Yang Liu Dan-Hua Shen Department of Pathology Peking University People’s HospitalBeijing 100044China

BACKGROUND Cellular myofibroma is a rare subtype of myofibroma that was first described in 2017.Its diagnosis is often challenging because of its relative rarity,lack of known genetic abnormalities,and expression of muscle markers that can be confused with sarcomas that have myogenic differentiation.Currently,scholars have limited knowledge of this disease,and published cases are few.Further accumulation of diagnostic and treatment experiences is required.CASE SUMMARY A 16-year-old girl experienced left upper limb swelling for 3 years.She sought medical attention at a local hospital 10 months ago,where magnetic resonance imaging revealed a 5-cm soft tissue mass.Needle biopsy performed at a local hospital resulted in the diagnosis of a spindle cell soft tissue sarcoma.The patient was referred to our hospital for limb salvage surgery with endoprosthetic replacement.She was initially diagnosed with a synovial sarcoma.Consequently,clinical management with chemotherapy was continued for the malignant sarcoma.Our pathology department also performed fluorescence in situ hybridization for result validation,which returned negative for SS18 gene breaks,indicating that it was not a synovial sarcoma.Next-generation sequencing was used to identify the SRF-RELA rearrangement.The final pathological diagnosis was a cellular/myofibroblastic neoplasm with an SRF-RELA gene fusion.The patient had initially received two courses of chemotherapy;however,chemotherapy was discontinued after the final diagnosis.CONCLUSION This case was misdiagnosed because of its rare occurrence,benign biological behavior,and pathological similarity to soft tissue sarcoma.

关键词： Cellular myofibroma SRF RELA fusion Pathology Case report

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Sleep Posture Classification Using RGB and Thermal Cameras Based on Deep Learning Model

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Computer Modeling in Engineering & Sciences 2024年第8期140卷 1729-1755页

作者： Awais Khan Chomyong Kim Jung-Yeon Kim Ahsan Aziz Yunyoung Nam Department of ICT Convergence Soonchunhyang UniversityAsan31538Korea ICT Convergence Research Center Soonchunhyang UniversityAsan31538Korea Emotional and Intelligent Child Care Convergence Research Center Soonchunhyang UniversityAsan31538Korea

Sleep posture surveillance is crucial for patient comfort,yet current systems face difficulties in providing compre-hensive studies due to the obstruction caused by blankets.Precise posture assessment remains challenging because of the complex nature of the human body and variations in sleep patterns.Consequently,this study introduces an innovative method utilizing RGB and thermal cameras for comprehensive posture classification,thereby enhancing the analysis of body position and comfort.This method begins by capturing a dataset of sleep postures in the form of videos using RGB and thermal cameras,which depict six commonly adopted postures:supine,left log,right log,prone head,prone left,and prone right.The study involves 10 participants under two conditions:with and without blankets.Initially,the database is normalized into a video frame.The subsequent step entails training a fine-tuned,pretrained Visual Geometry Group(VGG16)and ResNet50 model.In the third phase,the extracted features are utilized for classification.The fourth step of the proposed approach employs a serial fusion technique based on the normal distribution to merge the vectors derived from both the RGB and thermal datasets.Finally,the fused vectors are passed to machine learning classifiers for final classification.The dataset,which includes human sleep postures used in this study’s experiments,achieved a 96.7%accuracy rate using the Quadratic Support Vector Machine(QSVM)without the blanket.Moreover,the Linear SVM,when utilized with a blanket,attained an accuracy of 96%.When normal distribution serial fusion was applied to the blanket features,it resulted in a remarkable average accuracy of 99%.

关键词： Human sleep posture VGG16 deep learning ResNet50 fusion machine learning

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An Artificial Intelligence-Based Framework for Fruits Disease Recognition Using Deep Learning

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Computer Systems Science & Engineering 2024年第2期48卷 529-554页

作者： Irfan Haider Muhammad Attique Khan Muhammad Nazir Taerang Kim Jae-Hyuk Cha Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Computer Science Hanyang UniversitySeoulKorea

Fruit infections have an impact on both the yield and the quality of the crop.As a result,an automated recognition system for fruit leaf diseases is important.In artificial intelligence(AI)applications,especially in agriculture,deep learning shows promising disease detection and classification results.The recent AI-based techniques have a few challenges for fruit disease recognition,such as low-resolution images,small datasets for learning models,and irrelevant feature extraction.This work proposed a new fruit leaf leaf leaf disease recognition framework using deep learning features and improved pathfinder optimization.Three fruit types have been employed in this work for the validation process,such as apple,grape,and Citrus.In the first step,a noisy dataset is prepared by employing the original images to learn the designed framework better.The EfficientNet-B0 deep model is fine-tuned on the next step and trained separately on the original and noisy data.After that,features are fused using a serial concatenation approach that is later optimized in the next step using an improved Path Finder Algorithm(PFA).This algorithm aims to select the best features based on the fitness score and ignore redundant information.The selected features are finally classified using machine learning classifiers such as Medium Neural Network,Wide Neural Network,and Support Vector Machine.The experimental process was conducted on each fruit dataset separately and obtained an accuracy of 100%,99.7%,99.7%,and 93.4%for apple,grape,Citrus fruit,and citrus plant leaves,respectively.A detailed analysis is conducted and also compared with the recent techniques,and the proposed framework shows improved accuracy.

关键词： Fruit disease contrast enhancement augmentation deep learning fusion feature selection classification

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fusion of spatiotemporal and thematic features of textual data for animal disease surveillance

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Information Processing in Agriculture 2023年第3期10卷 347-360页

作者： Sarah Valentin Renaud Lancelot Mathieu Roche CIRAD F-34398 MontpellierFrance ASTRE Univ MontpellierCIRADINRAEMontpellierFrance TETIS Univ MontpellierAgroParisTechCIRADCNRSINRAEMontpellierFrance Departement de Biologie Universite de SherbrookeSherbrookeQuebecCanada

Several internet-based surveillance systems have been created to monitor the web for animal health surveillance.These systems collect a large amount of news dealing with outbreaks related to animal diseases.Automatically identifying news articles that describe the same outbreak event is a key step to quickly detect relevant epidemiological information while alleviating manual curation of news content.This paper addresses the task of retrieving news articles that are related in epidemiological terms.We tackle this issue using text mining and feature fusion methods.The main objective of this paper is to identify a textual representation in which two articles that share the same epidemiological content are close.We compared two types of representations(i.e.,features)to represent the documents:(i)morphosyntactic features(i.e.,selection and transformation of all terms from the news,based on classical textual processing steps)and(ii)lexicosemantic features(i.e.,selection,transformation and fusion of epidemiological terms including diseases,hosts,locations and dates).We compared two types of term weighing(i.e.,Boolean and TF-IDF)for both representations.To combine and transform lexicosemantic features,we compared two data fusion techniques(i.e.,early fusion and late fusion)and the effect of features generalisation,while evaluating the relative importance of each type of feature.We conducted our analysis using a corpus composed of a subset of news articles in English related to animal disease outbreaks.Our results showed that the combination of relevant lexicosemantic(epidemiological)features using fusion methods improves classical morphosyntactic representation in the context of disease-related news retrieval.The lexicosemantic representation based on TF-IDF and feature generalisation(F-measure=0.92,r-precision=0.58)outperformed the morphosyntactic representation(F-measure=0.89,r-precision=0.45),while reducing the features space.Converting the features into lower granular features(i.e.,generalisation)contributed to impr

关键词： Animal disease surveillance Text mining Ranking fusion

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Multimodal Biometric fusion Algorithm Based on Ranking Partition Collision Theory

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Machine Intelligence Research 2023年第6期20卷 884-896页

作者： Zhuorong Li Yunqi Tang School of Criminal Investigation People’s Public Security University of ChinaBeijing100038China

Score-based multimodal biometric fusion has been shown to be successful in addressing the problem of unimodal techniques’vulnerability to attack and poor performance in low-quality data.However,difficulties still exist in how to unify the meaning of heterogeneous scores more effectively.Aside from the matching scores themselves,the importance of the ranking information they include has been undervalued in previous studies.This study concentrates on matching scores and their ranking information and suggests the ranking partition collision(RPC)theory from the standpoint of the worth of scores.To meet both forensic and judicial needs,this paper proposes a method that employs a neural network to fuse biometrics at the score level.In addition,this paper constructs a virtual homologous dataset and conducts experiments on it.Experimental results demonstrate that the proposed method achieves an accuracy of 100%in both mAP and Rank1.To show the efficiency of the proposed method in practical applications,this work carries out more experiments utilizing real-world data.The results show that the proposed approach maintains a Rank1 accuracy of 99.2%on the million-scale database.It offers a novel approach to fusion at the score level.

关键词： Image processing convolutional neural network multimodal biometrics fusion

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Multi-layer collaborative optimization fusion for semi-supervised learning

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Chinese Journal of Aeronautics 2023年第11期36卷 342-353页

作者： Quanbo GE Muhua LIU Jianchao ZHANG Jianqiang SONG Junlong ZHU Mingchuan ZHANG School of Automation Nanjing University of Information Science and TechnologyNanjing 210044China School of Information Engineering Henan University of Science and TechnologyLuoyang 471023China Qiandao Lake Institute of Science Hangzhou 311799China

Recently,the Cooperative Training Algorithm(CTA),a well-known Semi-Supervised Learning(SSL)technique,has garnered significant attention in the field of image classification.However,traditional CTA approaches face challenges such as high computational complexity and low classification accuracy.To overcome these limitations,we present a novel approach called Weighted fusion based Cooperative Training Algorithm(W-CTA),which leverages the cooperative training technique and unlabeled data to enhance classification performance.Moreover,we introduce the K-means Cooperative Training Algorithm(km-CTA)to prevent the occurrence of local optima during the training phase.Finally,we conduct various experiments to verify the performance of the proposed methods.Experimental results show that W-CTA and km-CTA are effective and efficient on CIFAR-10 dataset.

关键词： Collaborative training fusion Image classification K-means algorithm Semi-supervised learning

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Cardiac Arrhythmia Disease Classifier Model Based on a Fuzzy fusion Approach

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Computers, Materials & Continua 2023年第5期75卷 4485-4499页

作者： Fatma Taher Hamoud Alshammari Lobna Osman Mohamed Elhoseny Abdulaziz Shehab Eman Elayat College of Technological Innovation Zayed UniversityDubaiUAE Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaSaudi Arabia Department of Electronics and Communications Engineering Delta Higher Institute for Engineering&TechnologyMansouraEgypt College of Computing and Informatics University of SharjahSharjahUnited Arab Emirates Department of Information Systems Mansoura UniversityMansoura35516Egypt Department of Teacher Preparation Faculty of Specific EducationMansoura UniversityMansouraEgypt

Cardiac diseases are one of the greatest global health challenges.Due to the high annual mortality rates,cardiac diseases have attracted the attention of numerous researchers in recent years.This article proposes a hybrid fuzzy fusion classification model for cardiac arrhythmia diseases.The fusion model is utilized to optimally select the highest-ranked features generated by a variety of well-known feature-selection algorithms.An ensemble of classifiers is then applied to the fusion’s results.The proposed model classifies the arrhythmia dataset from the University of California,Irvine into normal/abnormal classes as well as 16 classes of arrhythmia.Initially,at the preprocessing steps,for the miss-valued attributes,we used the average value in the linear attributes group by the same class and the most frequent value for nominal attributes.However,in order to ensure the model optimality,we eliminated all attributes which have zero or constant values that might bias the results of utilized classifiers.The preprocessing step led to 161 out of 279 attributes(features).Thereafter,a fuzzy-based feature-selection fusion method is applied to fuse high-ranked features obtained from different heuristic feature-selection algorithms.In short,our study comprises three main blocks:(1)sensing data and preprocessing;(2)feature queuing,selection,and extraction;and(3)the predictive model.Our proposed method improves classification performance in terms of accuracy,F1measure,recall,and precision when compared to state-of-the-art techniques.It achieves 98.5%accuracy for binary class mode and 98.9%accuracy for categorized class mode.

关键词： Cardiac arrhythmia preprocessing missing values classification model fusion

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Maize(Zea mays L.)seedling detection based on the fusion of a modified deep learning model and a novel Lidar points projecting strategy

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International Journal of Agricultural and Biological Engineering 2022年第5期15卷 172-180页

作者： Gang Wang Dongyan Huang Deyi Zhou Huili Liu Minghao Qu Zhongyang Ma College of Biological and Agricultural Engineering Jilin UniversityChangchun 130022China Key Laboratory of Bionic Engineering Ministry of EducationJilin UniversityChangchun 130022China

Accurate crop detection is the prerequisite for the operation of intelligent agricultural machinery.Image recognition usually lacks accurate orientation information,and Lidar point clouds are not easy to distinguish different objects.Fortunately,the fusion of images and Lidar points can complement each other.This research aimed to detect maize(Zea mays L.)seedlings by fusing Lidar data with images.By applying coordinate transformation and time stamps,the images and Lidar points were realized homogeneous in spatial as well as temporal dimensions.Deep learning was used to develop a maize seedling recognition model,then the model recognized maize seedlings by labeling them with bounding boxes.Meanwhile,Lidar points were mapped to the bounding boxes.Only one-third of points that fell into the right middle of bounding boxes were selected for clustering operation,the calculated center of the cluster provided spatial information for target maize seedlings.This study modified the classical single shot multi-box detector(SSD)by merely linking the last feature map to the final output layer,owing to the higher feature maps having the unique advantages of detecting relatively larger objects.In images,maize seedlings were just the largest objects owing to be shot on purpose.This modification enabled the recognition model to finish recognizing an image by only consuming around 60 ms,which saved about 10 ms/image compared with the classical SSD model.The experiment was conducted in a maize field,and the maize was during the elongation stage.Experimental results demonstrated that the standard deviations for maximum distance error and maximum angle error were 1.4 cm and 1.1°,respectively,which can be tolerated under current technical requirements.Since agricultural fields are subject to staple crop-orientated and changeable ambient environment,the fusion of images and Lidar points can derive more precision information,and make agricultural machinery smarter.This study can act as an upstream technology for other researches on inte

关键词： maize seedling detection fusion deep learning Lidar

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Gated fusion Based Transformer Model for Crack Detection on Wind Turbine Blade

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Energy Engineering 2023年第11期120卷 2667-2681页

作者： Wenyang Tang Cong Liu Bo Zhang School of Mechanical Engineering Yancheng Institute of TechnologyYancheng224007China Huaneng OffshoreWind Power Science and Technology Research Co. Ltd.Yancheng224007China

Harsh working environments and wear between blades and other unit components can easily lead to cracks and damage on wind turbine blades.The cracks on the blades can endanger the shafting of the generator set,the tower and other components,and even cause the tower to collapse.To achieve high-precision wind blade crack detection,this paper proposes a crack fault-detection strategy that integratesGated ResidualNetwork(GRN),a fusionmodule and Transformer.Firstly,GRNcan reduce unnecessary noisy inputs that could negatively impact performancewhile preserving the integrity of feature information.In addition,to gain in-depth information about the characteristics of wind turbine blades,a fusionmodule is suggested to implement the information fusion of wind turbine features.Specifically,each fan feature ismapped to a one-dimensional vector with the same length,and all one-dimensional vectors are concatenated to obtain a two-dimensional vector.And then,in the fusion module,the information fusion of the same characteristic variables in the different channels is realized through the Channel-mixing MLP,and the information fusion of different characteristic variables in the same channel is realized through the ColumnmixingMLP.Finally,the fused feature vector is input into the Transformer for feature learning,which enhances the influence of important feature information and improves the model’s anti-noise ability and classification accuracy.Extensive experimentswere conducted on the wind turbine supervisory control and data acquisition(SCADA)data froma domesticwind field.The results show that compared with other state-of-the-artmodels,including XGBoost,LightGBM,TabNet,etc.,the F1-score of proposed gated fusion based Transformer model can reach 0.9907,which is 0.4%-2.09% higher than the comparedmodels.Thismethod provides amore reliable approach for the condition detection and maintenance of fan blades in wind farms.

关键词： Crack detection gated residual network fusion attention

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