DCEL:classifier fusion model for Android malware detection

The rapid growth of mobile applications,the popularity of the Android system and its openness have attracted many hackers and even criminals,who are creating lots of Android malware.However,the current methods of Android malware detection need a lot of time in the feature engineering phase.Furthermore,these models have the defects of low detection rate,high complexity,and poor practicability,etc.We analyze the Android malware samples,and the distribution of malware and benign software in application programming interface(API)calls,permissions,and other attributes.We classify the software’s threat levels based on the correlation of features.Then,we propose deep neural networks and convolutional neural networks with ensemble learning(DCEL),a new classifier fusion model for Android malware detection.First,DCEL preprocesses the malware data to remove redundant data,and converts the one-dimensional data into a two-dimensional gray image.Then,the ensemble learning approach is used to combine the deep neural network with the convolutional neural network,and the final classification results are obtained by voting on the prediction of each single classifier.Experiments based on the Drebin and Malgenome datasets show that compared with current state-of-art models,the proposed DCEL has a higher detection rate,higher recall rate,and lower computational cost.

Physical-data Fusion Modeling Method for Energy Consumption Analysis of Smart Building

The energy consumption of buildings accounts for approximately 40%of total energy consumption.An accurate energy consumption analysis of buildings can not only promise significant energy savings but also help estimate the demand response potential more accurately,and consequently brings benefits to the upstream power grid.This paper proposes a novel physical-data fusion modeling(PFM)method for modeling smart buildings that can accurately assess energy consumption.First,a thermal process model of buildings and an electrical load model that focus on building heating,ventilation,and air conditioning(HVAC)systems are presented to analyze the thermal-electrical conversion process of energy consumption of buildings.Second,the PFM method is used to improve the accuracy of the energy consumption analysis model for buildings by modifying the parameters that are difficult to measure in the physical model(i.e.,it effectively modifies the electrical load model based on the proposed PFM method).Finally,case studies involving a real-world dataset recorded in a high-tech park in Changzhou,China,demonstrate that the proposed method exhibits superior performance with respect to the traditional physical modeling(TPM)method and data-driven modeling(DDM)method in terms of the achieved accuracy.

M-LFM:a multi-level fusion modeling method for shape−performance integrated digital twin of complex structure

As a virtual representation of a specific physical asset,the digital twin has great potential for realizing the life cycle maintenance management of a dynamic system.Nevertheless,the dynamic stress concentration is generated since the state of the dynamic system changes over time.This generation of dynamic stress concentration has hindered the exploitation of the digital twin to reflect the dynamic behaviors of systems in practical engineering applications.In this context,this paper is interested in achieving real-time performance prediction of dynamic systems by developing a new digital twin framework that includes simulation data,measuring data,multi-level fusion modeling(M-LFM),visualization techniques,and fatigue analysis.To leverage its capacity,the M-LFM method combines the advantages of different surrogate models and integrates simulation and measured data,which can improve the prediction accuracy of dynamic stress concentration.A telescopic boom crane is used as an example to verify the proposed framework for stress prediction and fatigue analysis of the complex dynamic system.The results show that the M-LFM method has better performance in the computational efficiency and calculation accuracy of the stress prediction compared with the polynomial response surface method and the kriging method.In other words,the proposed framework can leverage the advantages of digital twins in a dynamic system:damage monitoring,safety assessment,and other aspects and then promote the development of digital twins in industrial fields.

Fusion-Based Deep Learning Model for Automated Forest Fire Detection

Earth resource and environmental monitoring are essential areas that can be used to investigate the environmental conditions and natural resources supporting sustainable policy development,regulatory measures,and their implementation elevating the environment.Large-scale forest fire is considered a major harmful hazard that affects climate change and life over the globe.Therefore,the early identification of forest fires using automated tools is essential to avoid the spread of fire to a large extent.Therefore,this paper focuses on the design of automated forest fire detection using a fusion-based deep learning(AFFD-FDL)model for environmental monitoring.The AFFDFDL technique involves the design of an entropy-based fusion model for feature extraction.The combination of the handcrafted features using histogram of gradients(HOG)with deep features using SqueezeNet and Inception v3 models.Besides,an optimal extreme learning machine(ELM)based classifier is used to identify the existence of fire or not.In order to properly tune the parameters of the ELM model,the oppositional glowworm swarm optimization(OGSO)algorithm is employed and thereby improves the forest fire detection performance.A wide range of simulation analyses takes place on a benchmark dataset and the results are inspected under several aspects.The experimental results highlighted the betterment of the AFFD-FDL technique over the recent state of art techniques.

Intelligent Fish Behavior Classification Using Modified Invasive Weed Optimization with Ensemble Fusion Model

Accurate and rapid detection of fish behaviors is critical to perceive health and welfare by allowing farmers to make informed management deci-sions about recirculating the aquaculture system while decreasing labor.The classic detection approach involves placing sensors on the skin or body of the fish,which may interfere with typical behavior and welfare.The progress of deep learning and computer vision technologies opens up new opportunities to understand the biological basis of this behavior and precisely quantify behaviors that contribute to achieving accurate management in precision farming and higher production efficacy.This study develops an intelligent fish behavior classification using modified invasive weed optimization with an ensemble fusion(IFBC-MIWOEF)model.The presented IFBC-MIWOEF model focuses on identifying the distinct kinds of fish behavior classification.To accomplish this,the IFBC-MIWOEF model designs an ensemble of Deep Learning(DL)based fusion models such as VGG-19,DenseNet,and Effi-cientNet models for fish behavior classification.In addition,the hyperparam-eter tuning of the DL models is carried out using the MIWO algorithm,which is derived from the concepts of oppositional-based learning(OBL)and the IWO algorithm.Finally,the softmax(SM)layer at the end of the DL model categorizes the input into distinct fish behavior classes.The experimental validation of the IFBC-MIWOEF model is tested using fish videos,and the results are examined under distinct aspects.An Extensive comparative study pointed out the improved outcomes of the IFBC-MIWOEF model over recent approaches.

Leveraging Multimodal Ensemble Fusion-Based Deep Learning for COVID-19 on Chest Radiographs

Recently,COVID-19 has posed a challenging threat to researchers,scientists,healthcare professionals,and administrations over the globe,from its diagnosis to its treatment.The researchers are making persistent efforts to derive probable solutions formanaging the pandemic in their areas.One of the widespread and effective ways to detect COVID-19 is to utilize radiological images comprising X-rays and computed tomography(CT)scans.At the same time,the recent advances in machine learning(ML)and deep learning(DL)models show promising results in medical imaging.Particularly,the convolutional neural network(CNN)model can be applied to identifying abnormalities on chest radiographs.While the epidemic of COVID-19,much research is led on processing the data compared with DL techniques,particularly CNN.This study develops an improved fruit fly optimization with a deep learning-enabled fusion(IFFO-DLEF)model for COVID-19 detection and classification.The major intention of the IFFO-DLEF model is to investigate the presence or absence of COVID-19.To do so,the presented IFFODLEF model applies image pre-processing at the initial stage.In addition,the ensemble of three DL models such as DenseNet169,EfficientNet,and ResNet50,are used for feature extraction.Moreover,the IFFO algorithm with a multilayer perceptron(MLP)classification model is utilized to identify and classify COVID-19.The parameter optimization of the MLP approach utilizing the IFFO technique helps in accomplishing enhanced classification performance.The experimental result analysis of the IFFO-DLEF model carried out on the CXR image database portrayed the better performance of the presented IFFO-DLEF model over recent approaches.

Co-Estimation of State of Charge and Capacity for Lithium-Ion Batteries with Multi-Stage Model Fusion Method

Lithium-ion batteries(LIBs)have emerged as the preferred energy storage systems for various types of electric transports,including electric vehicles,electric boats,electric trains,and electric airplanes.The energy management of LIBs in electric transports for all-climate and long-life operation requires the accurate estimation of state of charge(SOC)and capacity in real-time.This study proposes a multistage model fusion algorithm to co-estimate SOC and capacity.Firstly,based on the assumption of a normal distribution,the mean and variance of the residual error from the model at different ageing levels are used to calculate the weight for the establishment of a fusion model with stable parameters.Secondly,a differential error gain with forward-looking ability is introduced into a proportional–integral observer(PIO)to accelerate convergence speed.Thirdly,a fusion algorithm is developed by combining a multistage model and proportional–integral–differential observer(PIDO)to co-estimate SOC and capacity under a complex application environment.Fourthly,the convergence and anti-noise performance of the fusion algorithm are discussed.Finally,the hardware-in-the-loop platform is set up to verify the performance of the fusion algorithm.The validation results of different aged LIBs over a wide range of temperature show that the presented fusion algorithm can realize a high-accuracy estimation of SOC and capacity with the relative errors within 2%and 3.3%,respectively.

Optimal Fusion-Based Handcrafted with Deep Features for Brain Cancer Classification

Brain cancer detection and classification is done utilizing distinct medical imaging modalities like computed tomography(CT),or magnetic resonance imaging(MRI).An automated brain cancer classification using computer aided diagnosis(CAD)models can be designed to assist radiologists.With the recent advancement in computer vision(CV)and deep learning(DL)models,it is possible to automatically detect the tumor from images using a computer-aided design.This study focuses on the design of automated Henry Gas Solubility Optimization with Fusion of Handcrafted and Deep Features(HGSO-FHDF)technique for brain cancer classification.The proposed HGSO-FHDF technique aims for detecting and classifying different stages of brain tumors.The proposed HGSO-FHDF technique involves Gabor filtering(GF)technique for removing the noise and enhancing the quality of MRI images.In addition,Tsallis entropy based image segmentation approach is applied to determine injured brain regions in the MRI image.Moreover,a fusion of handcrafted with deep features using Residual Network(ResNet)is utilized as feature extractors.Finally,HGSO algorithm with kernel extreme learning machine(KELM)model was utilized for identifying the presence of brain tumors.For examining the enhanced brain tumor classification performance,a comprehensive set of simulations take place on the BRATS 2015 dataset.

Artificial Intelligence-Based Fusion Model for Paddy Leaf Disease Detection and Classification

In agriculture,rice plant disease diagnosis has become a challenging issue,and early identification of this disease can avoid huge loss incurred from less crop productivity.Some of the recently-developed computer vision and Deep Learning(DL)approaches can be commonly employed in designing effective models for rice plant disease detection and classification processes.With this motivation,the current research work devises an Efficient Deep Learning based FusionModel for Rice Plant Disease(EDLFM-RPD)detection and classification.The aim of the proposed EDLFM-RPD technique is to detect and classify different kinds of rice plant diseases in a proficient manner.In addition,EDLFM-RPD technique involves median filtering-based preprocessing and K-means segmentation to determine the infected portions.The study also used a fusion of handcrafted Gray Level Co-occurrence Matrix(GLCM)and Inception-based deep features to derive the features.Finally,Salp Swarm Optimization with Fuzzy Support Vector Machine(FSVM)model is utilized for classification.In order to validate the enhanced outcomes of EDLFM-RPD technique,a series of simulations was conducted.The results were assessed under different measures.The obtained values infer the improved performance of EDLFM-RPD technique over recent approaches and achieved a maximum accuracy of 96.170%.

Fast Sentiment Analysis Algorithm Based on Double Model Fusion

Nowadays,as the number of textual data is exponentially increasing,sentiment analysis has become one of the most significant tasks in natural language processing(NLP)with increasing attention.Traditional Chinese sentiment analysis algorithms cannot make full use of the order information in context and are inefficient in sentiment inference.In this paper,we systematically reviewed the classic and representative works in sentiment analysis and proposed a simple but efficient optimization.First of all,FastText was trained to get the basic classification model,which can generate pre-trained word vectors as a by-product.Secondly,Bidirectional Long Short-Term Memory Network(Bi-LSTM)utilizes the generated word vectors for training and then merges with FastText to make comprehensive sentiment analysis.By combining FastText and Bi-LSTM,we have developed a new fast sentiment analysis,called FAST-BiLSTM,which consistently achieves a balance between performance and speed.In particular,experimental results based on the real datasets demonstrate that our algorithm can effectively judge sentiments of users’comments,and is superior to the traditional algorithm in time efficiency,accuracy,recall and F1 criteria.

Chaotic Flower Pollination with Deep Learning Based COVID-19 Classification Model

The Coronavirus Disease(COVID-19)pandemic has exposed the vulnerabilities of medical services across the globe,especially in underdeveloped nations.In the aftermath of the COVID-19 outbreak,a strong demand exists for developing novel computer-assisted diagnostic tools to execute rapid and cost-effective screenings in locations where many screenings cannot be executed using conventional methods.Medical imaging has become a crucial component in the disease diagnosis process,whereas X-rays and Computed Tomography(CT)scan imaging are employed in a deep network to diagnose the diseases.In general,four steps are followed in image-based diagnostics and disease classification processes by making use of the neural networks,such as network training,feature extraction,model performance testing and optimal feature selection.The current research article devises a Chaotic Flower Pollination Algorithm with a Deep Learning-Driven Fusion(CFPADLDF)approach for detecting and classifying COVID-19.The presented CFPA-DLDF model is developed by integrating two DL models to recognize COVID-19 in medical images.Initially,the proposed CFPA-DLDF technique employs the Gabor Filtering(GF)approach to pre-process the input images.In addition,a weighted voting-based ensemble model is employed for feature extraction,in which both VGG-19 and the MixNet models are included.Finally,the CFPA with Recurrent Neural Network(RNN)model is utilized for classification,showing the work’s novelty.A comparative analysis was conducted to demonstrate the enhanced performance of the proposed CFPADLDF model,and the results established the supremacy of the proposed CFPA-DLDF model over recent approaches.

Predictive Multimodal Deep Learning-Based Sustainable Renewable and Non-Renewable Energy Utilization

Recently,renewable energy(RE)has become popular due to its benefits,such as being inexpensive,low-carbon,ecologically friendly,steady,and reliable.The RE sources are gradually combined with non-renewable energy(NRE)sources into electric grids to satisfy energy demands.Since energy utilization is highly related to national energy policy,energy prediction using artificial intelligence(AI)and deep learning(DL)based models can be employed for energy prediction on RE and NRE power resources.Predicting energy consumption of RE and NRE sources using effective models becomes necessary.With this motivation,this study presents a new multimodal fusionbased predictive tool for energy consumption prediction(MDLFM-ECP)of RE and NRE power sources.Actual data may influence the prediction performance of the results in prediction approaches.The proposed MDLFMECP technique involves pre-processing,fusion-based prediction,and hyperparameter optimization.In addition,the MDLFM-ECP technique involves the fusion of four deep learning(DL)models,namely long short-termmemory(LSTM),bidirectional LSTM(Bi-LSTM),deep belief network(DBN),and gated recurrent unit(GRU).Moreover,the chaotic cat swarm optimization(CCSO)algorithm is applied to tune the hyperparameters of the DL models.The design of the CCSO algorithm for optimal hyperparameter tuning of the DL models,showing the novelty of the work.A series of simulations took place to validate the superior performance of the proposed method,and the simulation outcome emphasized the improved results of the MDLFM-ECP technique over the recent approaches with minimum overall mean absolute percentage error of 3.58%.

Deep Learning Based Energy Consumption Prediction on Internet of Things Environment

The creation of national energy strategy cannot proceed without accurate projections of future electricity consumption;this is because EC is intimately tied to other forms of energy,such as oil and natural gas.For the purpose of determining and bettering overall energy consumption,there is an urgent requirement for accurate monitoring and calculation of EC at the building level using cutting-edge technology such as data analytics and the internet of things(IoT).Soft computing is a subset of AI that tries to design procedures that are more accurate and reliable,and it has proven to be an effective tool for solving a number of issues that are associated with the use of energy.The use of soft computing for energy prediction is an essential part of the solution to these kinds of challenges.This study presents an improved version of the Harris Hawks Optimization model by combining it with the IHHODL-ECP algorithm for use in Internet of Things settings.The IHHODL-ECP model that has been supplied acts as a useful instrument for the prediction of integrated energy consumption.In order for the raw electrical data to be compatible with the subsequent processing in the IHHODL-ECP model,it is necessary to perform a preprocessing step.The technique of prediction uses a combination of three different kinds of deep learning models,namely DNN,GRU,and DBN.In addition to this,the IHHO algorithm is used as a technique for making adjustments to the hyperparameters.The experimental result analysis of the IHHODL-ECP model is carried out under a variety of different aspects,and the comparison inquiry highlighted the advantages of the IHHODL-ECP model over other present approaches.According to the findings of the experiments conducted with an hourly time resolution,the IHHODL-ECP model obtained a MAPE value of 33.85,which was lower than those produced by the LR,LSTM,and CNN-LSTM models,which had MAPE values of 83.22,44.57,and 34.62 respectively.These findings provided evidence of the IHHODL-ECP model’s improved ability to provide accurate forecasts.

Sailfish Optimization with Deep Learning Based Oral Cancer Classification Model

Recently,computer aided diagnosis(CAD)model becomes an effective tool for decision making in healthcare sector.The advances in computer vision and artificial intelligence(AI)techniques have resulted in the effective design of CAD models,which enables to detection of the existence of diseases using various imaging modalities.Oral cancer(OC)has commonly occurred in head and neck globally.Earlier identification of OC enables to improve survival rate and reduce mortality rate.Therefore,the design of CAD model for OC detection and classification becomes essential.Therefore,this study introduces a novel Computer Aided Diagnosis for OC using Sailfish Optimization with Fusion based Classification(CADOC-SFOFC)model.The proposed CADOC-SFOFC model determines the existence of OC on the medical images.To accomplish this,a fusion based feature extraction process is carried out by the use of VGGNet-16 and Residual Network(ResNet)model.Besides,feature vectors are fused and passed into the extreme learning machine(ELM)model for classification process.Moreover,SFO algorithm is utilized for effective parameter selection of the ELM model,consequently resulting in enhanced performance.The experimental analysis of the CADOC-SFOFC model was tested on Kaggle dataset and the results reported the betterment of the CADOC-SFOFC model over the compared methods with maximum accuracy of 98.11%.Therefore,the CADOC-SFOFC model has maximum potential as an inexpensive and non-invasive tool which supports screening process and enhances the detection efficiency.

A Novel Deep Neural Network for Intracranial Haemorrhage Detection and Classification

Data fusion is one of the challenging issues,the healthcare sector is facing in the recent years.Proper diagnosis from digital imagery and treatment are deemed to be the right solution.Intracerebral Haemorrhage(ICH),a condition characterized by injury of blood vessels in brain tissues,is one of the important reasons for stroke.Images generated by X-rays and Computed Tomography(CT)are widely used for estimating the size and location of hemorrhages.Radiologists use manual planimetry,a time-consuming process for segmenting CT scan images.Deep Learning(DL)is the most preferred method to increase the efficiency of diagnosing ICH.In this paper,the researcher presents a unique multi-modal data fusion-based feature extraction technique with Deep Learning(DL)model,abbreviated as FFE-DL for Intracranial Haemorrhage Detection and Classification,also known as FFEDL-ICH.The proposed FFEDL-ICH model has four stages namely,preprocessing,image segmentation,feature extraction,and classification.The input image is first preprocessed using the Gaussian Filtering(GF)technique to remove noise.Secondly,the Density-based Fuzzy C-Means(DFCM)algorithm is used to segment the images.Furthermore,the Fusion-based Feature Extraction model is implemented with handcrafted feature(Local Binary Patterns)and deep features(Residual Network-152)to extract useful features.Finally,Deep Neural Network(DNN)is implemented as a classification technique to differentiate multiple classes of ICH.The researchers,in the current study,used benchmark Intracranial Haemorrhage dataset and simulated the FFEDL-ICH model to assess its diagnostic performance.The findings of the study revealed that the proposed FFEDL-ICH model has the ability to outperform existing models as there is a significant improvement in its performance.For future researches,the researcher recommends the performance improvement of FFEDL-ICH model using learning rate scheduling techniques for DNN.

Evolutionary Intelligence and Deep Learning Enabled Diabetic Retinopathy Classification Model

Diabetic Retinopathy(DR)has become a widespread illness among diabetics across the globe.Retinal fundus images are generally used by physicians to detect and classify the stages of DR.Since manual examination of DR images is a time-consuming process with the risks of biased results,automated tools using Artificial Intelligence(AI)to diagnose the disease have become essential.In this view,the current study develops an Optimal Deep Learning-enabled Fusion-based Diabetic Retinopathy Detection and Classification(ODL-FDRDC)technique.The intention of the proposed ODLFDRDC technique is to identify DR and categorize its different grades using retinal fundus images.In addition,ODL-FDRDC technique involves region growing segmentation technique to determine the infected regions.Moreover,the fusion of two DL models namely,CapsNet and MobileNet is used for feature extraction.Further,the hyperparameter tuning of these models is also performed via Coyote Optimization Algorithm(COA).Gated Recurrent Unit(GRU)is also utilized to identify DR.The experimental results of the analysis,accomplished by ODL-FDRDC technique against benchmark DR dataset,established the supremacy of the technique over existing methodologies under different measures.

Machine Learning Based Analysis of Real-Time Geographical of RS Spatio-Temporal Data

Flood disasters can be reliablymonitored using remote sensing photos with great spatiotemporal resolution.However,satellite revisit periods and extreme weather limit the use of high spatial resolution images.As a result,this research provides a method for combining Landsat and MODIS pictures to produce high spatiotemporal imagery for flood disaster monitoring.Using the spatial and temporal adaptive reflectance fusion model(STARFM),the spatial and temporal reflectance unmixingmodel(STRUM),and three prominent algorithms of flexible spatiotemporal data fusion(FSDAF),Landsat fusion images are created by fusing MODIS and Landsat images.Then,to extract flood information,utilize a support vector machine(SVM)to classify the fusion images.Assess the accuracy of your work.Experimental results suggest that the three spatio-temporal fusion algorithms may be used to effectively monitor floods,with FSDAF’s fusion results outperforming STARFM and STRUM in both study areas.The overall flood classification accuracy of the three STARFM,STRUM,and FSDAF algorithms in the Gwydir research region is 0.89,0.90,and 0.91,respectively,with Kappa coefficients of 0.63,0.64,and 0.67.The flood classification accuracy of the three fusion algorithms in the New Orleans research region is 0.90,0.89,and 0.91,with Kappa values of 0.77,0.76,and 0.81,respectively.The spatio-temporal fusion technique can be used to successfully monitor floods,according to this study.

Key technologies of artificial intelligence in electric power customer service

As the demand for customer service continues to increase,more companies are attempting to apply artificial intelligence technology in the field of customer service,enabling intelligent customer service,reducing customer service pressure,and reducing operating costs.Currently,the existing intelligent customer service has a limited degree of intelligence and can only answer simple user questions,and complex user expressions are difficult to understand.To solve the problem of low accuracy of multi-round dialogue semantic understanding,this paper proposes a semantic understanding model based on the fusion of a convolutional neural network(CNN)and attention.The model builds an“intention-slot”joint model based on the“encoding–decoding”framework and uses hidden semantic information that combines intent recognition and slot filling,avoiding the problem of information loss in traditional isolated tasks,and achieving end-to-end semantic understanding.Additionally,an improved attention mechanism based on CNNs is introduced in the decoding process to reduce the interference of redundant information in the original text,thereby increasing the accuracy of semantic understanding.Finally,by applying the model to electric power intelligent customer service,we verified through an experimental comparison that the proposed fusion model improves the performance of intent recognition and slot filling and can improve the user experience of electric power intelligent customer services.

Rapid identification of healthy Tegillarca granosa using laser-induced breakdown spectroscopy and fusion model

Objectives:This study presents a method combining a one-class classifier and laser-induced breakdown spectrometry(LIBS)to quickly identify healthy Tegillarca granosa(T.granosa).Materials and Methods:The sum of ranking differences(SRD)was used to fuse multiple anomaly detection metrics to build the one-class classifier,which was only trained with healthy T.granosa.The one-class classifier can identify healthy T.granosa to exclude non-healthy T.granosa.The proposed method calculated multiple anomaly detection metrics and standardized them to obtain a fusion matrix.Based on the fusion matrix,the samples were ranked by SRD and those ranked lowest and below the threshold were considered to be unhealthy.Results:Multiple anomaly detection metrics were fused by the SRD algorithm and tested on each band,and the final fusion model achieved an accuracy rate of 98.46%,a sensitivity of 100%,and a specificity of 80%.The remaining three single classification models obtained the following results:the SVDD model achieved an accuracy rate of 87.69%,a sensitivity of 90%,and a specificity of 60%;the OCSVM model achieved an accuracy rate of 80%,a sensitivity of 76.67%,and a specificity of 60%;and the DD-SIMCA model achieved an accuracy rate of 95.38%,a sensitivity of 98.33%,and a specificity of 60%.Conclusions:The experimental results showed that the proposed method achieved better results than the traditional one-class classification methods with a single metric.Therefore,the fusion method effectively improves the performance of traditional one-class classifiers when using LIBS to quickly identify healthy substances(healthy T.granosa).

Multiple Detection Model Fusion Framework for Printed Circuit Board Defect Detection

The printed circuit board(PCB)is an indispensable component of electronic products,which deter-mines the quality of these products.With the development and advancement of manufacturing technology,the layout and structure of PCB are getting complicated.However,there are few effective and accurate PCB defect detection methods.There are high requirements for the accuracy of PCB defect detection in the actual pro-duction environment,so we propose two PCB defect detection frameworks with multiple model fusion including the defect detection by multi-model voting method(DDMV)and the defect detection by multi-model learning method(DDML).With the purpose of reducing wrong and missing detection,the DDMV and DDML integrate multiple defect detection networks with different fusion strategies.The effectiveness and accuracy of the proposed framework are verified with extensive experiments on two open-source PCB datasets.The experimental results demonstrate that the proposed DDMV and DDML are better than any other individual state-of-the-art PCB defect detection model in F1-score,and the area under curve value of DDML is also higher than that of any other individual detection model.Furthermore,compared with DDMV,the DDML with an automatic machine learning method achieves the best performance in PCB defect detection,and the Fl-score on the two datasets can reach 99.7%and 95.6%respectively.