Analysis of learnability of a novel hybrid quantum-classical convolutional neural network in image classification

We design a new hybrid quantum-classical convolutional neural network(HQCCNN)model based on parameter quantum circuits.In this model,we use parameterized quantum circuits(PQCs)to redesign the convolutional layer in classical convolutional neural networks,forming a new quantum convolutional layer to achieve unitary transformation of quantum states,enabling the model to more accurately extract hidden information from images.At the same time,we combine the classical fully connected layer with PQCs to form a new hybrid quantum-classical fully connected layer to further improve the accuracy of classification.Finally,we use the MNIST dataset to test the potential of the HQCCNN.The results indicate that the HQCCNN has good performance in solving classification problems.In binary classification tasks,the classification accuracy of numbers 5 and 7 is as high as 99.71%.In multivariate classification,the accuracy rate also reaches 98.51%.Finally,we compare the performance of the HQCCNN with other models and find that the HQCCNN has better classification performance and convergence speed.

Stacking Ensemble Learning-Based Convolutional Gated Recurrent Neural Network for Diabetes Miletus

Diabetes mellitus is a metabolic disease in which blood glucose levels rise as a result of pancreatic insulin production failure.It causes hyperglycemia and chronic multiorgan dysfunction,including blindness,renal failure,and cardi-ovascular disease,if left untreated.One of the essential checks that are needed to be performed frequently in Type 1 Diabetes Mellitus is a blood test,this procedure involves extracting blood quite frequently,which leads to subject discomfort increasing the possibility of infection when the procedure is often recurring.Exist-ing methods used for diabetes classification have less classification accuracy and suffer from vanishing gradient problems,to overcome these issues,we proposed stacking ensemble learning-based convolutional gated recurrent neural network(CGRNN)Metamodel algorithm.Our proposed method initially performs outlier detection to remove outlier data,using the Gaussian distribution method,and the Box-cox method is used to correctly order the dataset.After the outliers’detec-tion,the missing values are replaced by the data’s mean rather than their elimina-tion.In the stacking ensemble base model,multiple machine learning algorithms like Naïve Bayes,Bagging with random forest,and Adaboost Decision tree have been employed.CGRNN Meta model uses two hidden layers Long-Short-Time Memory(LSTM)and Gated Recurrent Unit(GRU)to calculate the weight matrix for diabetes prediction.Finally,the calculated weight matrix is passed to the soft-max function in the output layer to produce the diabetes prediction results.By using LSTM-based CG-RNN,the mean square error(MSE)value is 0.016 and the obtained accuracy is 91.33%.

Lightweight and highly robust memristor-based hybrid neural networks for electroencephalogram signal processing

Memristor-based neuromorphic computing shows great potential for high-speed and high-throughput signal processing applications,such as electroencephalogram(EEG)signal processing.Nonetheless,the size of one-transistor one-resistor(1T1R)memristor arrays is limited by the non-ideality of the devices,which prevents the hardware implementation of large and complex networks.In this work,we propose the depthwise separable convolution and bidirectional gate recurrent unit(DSC-BiGRU)network,a lightweight and highly robust hybrid neural network based on 1T1R arrays that enables efficient processing of EEG signals in the temporal,frequency and spatial domains by hybridizing DSC and BiGRU blocks.The network size is reduced and the network robustness is improved while ensuring the network classification accuracy.In the simulation,the measured non-idealities of the 1T1R array are brought into the network through statistical analysis.Compared with traditional convolutional networks,the network parameters are reduced by 95%and the network classification accuracy is improved by 21%at a 95%array yield rate and 5%tolerable error.This work demonstrates that lightweight and highly robust networks based on memristor arrays hold great promise for applications that rely on low consumption and high efficiency.

Mapping Network-Coordinated Stacked Gated Recurrent Units for Turbulence Prediction

Accurately predicting fluid forces acting on the sur-face of a structure is crucial in engineering design.However,this task becomes particularly challenging in turbulent flow,due to the complex and irregular changes in the flow field.In this study,we propose a novel deep learning method,named mapping net-work-coordinated stacked gated recurrent units(MSU),for pre-dicting pressure on a circular cylinder from velocity data.Specifi-cally,our coordinated learning strategy is designed to extract the most critical velocity point for prediction,a process that has not been explored before.In our experiments,MSU extracts one point from a velocity field containing 121 points and utilizes this point to accurately predict 100 pressure points on the cylinder.This method significantly reduces the workload of data measure-ment in practical engineering applications.Our experimental results demonstrate that MSU predictions are highly similar to the real turbulent data in both spatio-temporal and individual aspects.Furthermore,the comparison results show that MSU predicts more precise results,even outperforming models that use all velocity field points.Compared with state-of-the-art methods,MSU has an average improvement of more than 45%in various indicators such as root mean square error(RMSE).Through comprehensive and authoritative physical verification,we estab-lished that MSU’s prediction results closely align with pressure field data obtained in real turbulence fields.This confirmation underscores the considerable potential of MSU for practical applications in real engineering scenarios.The code is available at https://github.com/zhangzm0128/MSU.

Enhancing Human Action Recognition with Adaptive Hybrid Deep Attentive Networks and Archerfish Optimization

In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant attention.Previously developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basic features.The images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human actions.Poor lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging task.The unimodal-based HAR approaches are not suitable in a real-time environment.Therefore,an updated HAR model is developed using multiple types of data and an advanced deep-learning approach.Firstly,the required signals and sensor data are accumulated from the standard databases.From these signals,the wave features are retrieved.Then the extracted wave features and sensor data are given as the input to recognize the human activity.An Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated Recurrent Unit(GRU)”for the human activity recognition process.Additionally,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition process.An experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR model.The EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,respectively.The result proved that the developed model is effective in recognizing human action by taking less time.Additionally,it reduces the computation complexity and overfitting issue through using an optimization approach.

Fake News Classification Using a Fuzzy Convolutional Recurrent Neural Network

In recent years,social media platforms have gained immense popularity.As a result,there has been a tremendous increase in content on social media platforms.This content can be related to an individual’s sentiments,thoughts,stories,advertisements,and news,among many other content types.With the recent increase in online content,the importance of identifying fake and real news has increased.Although,there is a lot of work present to detect fake news,a study on Fuzzy CRNN was not explored into this direction.In this work,a system is designed to classify fake and real news using fuzzy logic.The initial feature extraction process is done using a convolutional recurrent neural network(CRNN).After the extraction of features,word indexing is done with high dimensionality.Then,based on the indexing measures,the ranking process identifies whether news is fake or real.The fuzzy CRNN model is trained to yield outstanding resultswith 99.99±0.01%accuracy.This work utilizes three different datasets(LIAR,LIAR-PLUS,and ISOT)to find the most accurate model.

Recurrent Convolutional Neural Network MSER-Based Approach for Payable Document Processing

A tremendous amount of vendor invoices is generated in the corporate sector.To automate the manual data entry in payable documents,highly accurate Optical Character Recognition(OCR)is required.This paper proposes an end-to-end OCR system that does both localization and recognition and serves as a single unit to automate payable document processing such as cheques and cash disbursement.For text localization,the maximally stable extremal region is used,which extracts a word or digit chunk from an invoice.This chunk is later passed to the deep learning model,which performs text recognition.The deep learning model utilizes both convolution neural networks and long short-term memory(LSTM).The convolution layer is used for extracting features,which are fed to the LSTM.The model integrates feature extraction,modeling sequence,and transcription into a unified network.It handles the sequences of unconstrained lengths,independent of the character segmentation or horizontal scale normalization.Furthermore,it applies to both the lexicon-free and lexicon-based text recognition,and finally,it produces a comparatively smaller model,which can be implemented in practical applications.The overall superior performance in the experimental evaluation demonstrates the usefulness of the proposed model.The model is thus generic and can be used for other similar recognition scenarios.

Deep Convolutional Neural Network Based Churn Prediction for Telecommunication Industry

Currently,mobile communication is one of the widely used means of communication.Nevertheless,it is quite challenging for a telecommunication company to attract new customers.The recent concept of mobile number portability has also aggravated the problem of customer churn.Companies need to identify beforehand the customers,who could potentially churn out to the competitors.In the telecommunication industry,such identification could be done based on call detail records.This research presents an extensive experimental study based on various deep learning models,such as the 1D convolutional neural network(CNN)model along with the recurrent neural network(RNN)and deep neural network(DNN)for churn prediction.We use the mobile telephony churn prediction dataset obtained from customers-dna.com,containing the data for around 100,000 individuals,out of which 86,000 are non-churners,whereas 14,000 are churned customers.The imbalanced data are handled using undersampling and oversampling.The accuracy for CNN,RNN,and DNN is 91%,93%,and 96%,respectively.Furthermore,DNN got 99%for ROC.

Text Understanding with a Hybrid Neural Network Based Learning

Teaching machine to understand needs to design an algorithm for the machine to comprehend documents. As some traditional methods cannot learn the inherent characters effectively, this paper presents a new hybrid neural network model to extract sentence-level summarization from single document,and it allows us to develop an attention based deep neural network that can learn to understand documents with minimal prior knowledge. The proposed model composed of multiple processing layers can learn the representations of features.Word embedding is used to learn continuous word representations for constructing sentence as input to convolutional neural network. The recurrent neural network is also used to label the sentences from the original document, and the proposed BAM-GRU model is more efficient. Experimental results show the feasibility of the approach. Some problems and further works are also present in the end.

Improved Quantile Convolutional and Recurrent Neural Networks for Electric Vehicle Battery Temperature Prediction

The battery thermal management of electric vehicles can be improved using neural networks predicting quantile sequences of the battery temperature.This work extends a method for the development of Quantile Convolutional and Quantile Recurrent Neural Networks(namely Q*NN).Fleet data of 225629 drives are clustered and balanced,simulation data from 971 simulations are augmented before they are combined for training and testing.The Q*NN hyperparameters are optimized using an efficient Bayesian optimization,before the Q*NN models are compared with regression and quantile regression models for four horizons.The analysis of point-forecast and quantile-related metrics shows the superior performance of the novel Q*NN models.The median predictions of the best performing model achieve an average RMSE of 0.66°C and R^(2) of 0.84.The predicted 0.99 quantile covers 98.87%of the true values in the test data.In conclusion,this work proposes an extended development and comparison of Q*NN models for accurate battery temperature prediction.

Deep learning neural networks for spatially explicit prediction of flash flood probability

Flood probability maps are essential for a range of applications,including land use planning and developing mitigation strategies and early warning systems.This study describes the potential application of two architectures of deep learning neural networks,namely convolutional neural networks(CNN)and recurrent neural networks(RNN),for spatially explicit prediction and mapping of flash flood probability.To develop and validate the predictive models,a geospatial database that contained records for the historical flood events and geo-environmental characteristics of the Golestan Province in northern Iran was constructed.The step-wise weight assessment ratio analysis(SWARA)was employed to investigate the spatial interplay between floods and different influencing factors.The CNN and RNN models were trained using the SWARA weights and validated using the receiver operating characteristics technique.The results showed that the CNN model(AUC=0.832,RMSE=0.144)performed slightly better than the RNN model(AUC=0.814,RMSE=0.181)in predicting future floods.Further,these models demonstrated an improved prediction of floods compared to previous studies that used different models in the same study area.This study showed that the spatially explicit deep learning neural network models are successful in capturing the heterogeneity of spatial patterns of flood probability in the Golestan Province,and the resulting probability maps can be used for the development of mitigation plans in response to the future floods.The general policy implication of our study suggests that design,implementation,and verification of flood early warning systems should be directed to approximately 40%of the land area characterized by high and very susceptibility to flooding.

Multi-Scale Convolutional Gated Recurrent Unit Networks for Tool Wear Prediction in Smart Manufacturing

As an integrated application of modern information technologies and artificial intelligence,Prognostic and Health Management(PHM)is important for machine health monitoring.Prediction of tool wear is one of the symbolic applications of PHM technology in modern manufacturing systems and industry.In this paper,a multi-scale Convolutional Gated Recurrent Unit network(MCGRU)is proposed to address raw sensory data for tool wear prediction.At the bottom of MCGRU,six parallel and independent branches with different kernel sizes are designed to form a multi-scale convolutional neural network,which augments the adaptability to features of different time scales.These features of different scales extracted from raw data are then fed into a Deep Gated Recurrent Unit network to capture long-term dependencies and learn significant representations.At the top of the MCGRU,a fully connected layer and a regression layer are built for cutting tool wear prediction.Two case studies are performed to verify the capability and effectiveness of the proposed MCGRU network and results show that MCGRU outperforms several state-of-the-art baseline models.

A Neural Network Approach for Misuse and Anomaly Intrusion Detection

An MI.P(Multi-Layer Perception)/Elman neural network is proposed in thispaper, which realizes classification with memory of past events using the real-time classificationof MI.P and the memorial functionality of Elman. The system's sensitivity for the memory of pastevents ean be easily reconfigured without retraining the whole network. This approach can he usedfor both misuse and anomaly detection system. The intrusion detection systems(TDSs) using the hybridMLP/Elman neural network are evaluated by the intrusion detection evaluation data sponsored by U.S.Defense Advanced Research Projects Agency CDARPA) Ihc results of experiment are presented inReceiver Operating Characteristic CROC) curves. Thc capabilites of these IDSs to identify DenyofService(DOS) and probing attacks are enhanced.

Multimodal emotion recognition based on deep neural network

In order to increase the accuracy rate of emotion recognition in voiceand video,the mixed convolutional neural network(CNN)and recurrent neural network(RNN)ae used to encode and integrate the two information sources.For the audio signals,several frequency bands as well as some energy functions are extacted as low-level features by using a sophisticated audio technique,and then they are encoded w it a one-dimensional(I D)convolutional neural network to abstact high-level features.Finally,tiese are fed into a recurrent neural network for te sake of capturing dynamic tone changes in a temporal dimensionality.As a contrast,a two-dimensional(2D)convolutional neural network and a similar RNN are used to capture dynamic facial appearance changes of temporal sequences.The method was used in te Chinese Natral Audio-'Visual Emotion Database in te Chinese Conference on Pattern Recognition(CCPR)in2016.Experimental results demonstrate that te classification average precision of the proposed metiod is41.15%,which is increased by16.62%compaed with te baseline algorithm offered by the CCPR in2016.It is proved ta t te proposed method has higher accuracy in te identification of emotional information.

Deep Neural Network-Based Chinese Semantic Role Labeling

A recent trend in machine learning is to use deep architectures to discover multiple levels of features from data,which has achieved impressive results on various natural language processing(NLP)tasks.We propose a deep neural network-based solution to Chinese semantic role labeling(SRL)with its application on message analysis.The solution adopts a six-step strategy:text normalization,named entity recognition(NER),Chinese word segmentation and part-of-speech(POS)tagging,theme classification,SRL,and slot filling.For each step,a novel deep neural network-based model is designed and optimized,particularly for smart phone applications.Experiment results on all the NLP sub-tasks of the solution show that the proposed neural networks achieve state-of-the-art performance with the minimal computational cost.The speed advantage of deep neural networks makes them more competitive for large-scale applications or applications requiring real-time response,highlighting the potential of the proposed solution for practical NLP systems.

Deep-fake video detection approaches using convolutional–recurrent neural networks

Deep-Fake is an emerging technology used in synthetic media which manipulates individuals in existing images and videos with someone else’s likeness.This paper presents the comparative study of different deep neural networks employed for Deep-Fake video detection.In the model,the features from the training data are extracted with the intended Convolution Neural Network model to form feature vectors which are further analysed using a dense layer,a Long Short-Term Memoryand Gated Recurrent by adopting transfer learning with fine tuning for training the models.The model is evaluated to detect Artificial Intelligence based Deep fakes images and videos using benchmark datasets.Comparative analysis shows that the detections are majorly biased towards domain of the dataset but there is a noteworthy improvement in the model performance parameters by using Transfer Learning whereas Convolutional-Recurrent Neural Network has benefits in sequence detection.

A Convolutional Deep Neural Network Approach for miRNA Clustering

The regulatory role of the Micro-RNAs (miRNAs) in the messenger RNAs (mRNAs) gene expression is well understood by the biologists since some decades, even though the delving into specific aspects is in progress. Clustering is a cornerstone in bioinformatics research, offering a potent computational tool for analyzing diverse types of data encountered in genomics and related fields. MiRNA clustering plays a pivotal role in deciphering the intricate regulatory roles of miRNAs in biological systems. It uncovers novel biomarkers for disease diagnosis and prognosis and advances our understanding of gene regulatory networks and pathways implicated in health and disease, as well as drug discovery. Namely, we have implemented clustering procedure to find interrelations among miRNAs within clusters, and their relations to diseases. Deep clustering (DC) algorithms signify a departure from traditional clustering methods towards more sophisticated techniques, that can uncover intricate patterns and relationships within gene expression data. Deep learning (DL) models have shown remarkable success in various domains, and their application in genomics, especially for tasks like clustering, holding immense promise. The deep convolutional clustering procedure used is different from other traditional methods, demonstrating unbiased clustering results. In the paper, we implement the procedure on a Multiple Myeloma miRNA dataset publicly available on GEO platform, as a template of a cancer instance analysis, and hazard some biological issues.

A Hybrid Deep Learning Approach for Green Energy Forecasting in Asian Countries

Electricity is essential for keeping power networks balanced between supply and demand,especially since it costs a lot to store.The article talks about different deep learning methods that are used to guess how much green energy different Asian countries will produce.The main goal is to make reliable and accurate predictions that can help with the planning of new power plants to meet rising demand.There is a new deep learning model called the Green-electrical Production Ensemble(GP-Ensemble).It combines three types of neural networks:convolutional neural networks(CNNs),gated recurrent units(GRUs),and feedforward neural networks(FNNs).The model promises to improve prediction accuracy.The 1965–2023 dataset covers green energy generation statistics from ten Asian countries.Due to the rising energy supply-demand mismatch,the primary goal is to develop the best model for predicting future power production.The GP-Ensemble deep learning model outperforms individual models(GRU,FNN,and CNN)and alternative approaches such as fully convolutional networks(FCN)and other ensemble models in mean squared error(MSE),mean absolute error(MAE)and root mean squared error(RMSE)metrics.This study enhances our ability to predict green electricity production over time,with MSE of 0.0631,MAE of 0.1754,and RMSE of 0.2383.It may influence laws and enhance energy management.

Modeling landslide susceptibility based on convolutional neural network coupling with metaheuristic optimization algorithms

Landslides are one of the most common geological hazards worldwide,especially in Sichuan Province(Southwest China).The current study's main,purposes are to explore the potential applications of convolutional neural networks(CNN)hybrid ensemble metaheuristic optimization algorithms,namely beluga whale optimization(BWO)and coati optimization algorithm(COA),for landslide susceptibility mapping in Sichuan Province(China).For this aim,fourteen landslide conditioning factors were compiled in a spatial database.The effectiveness of the conditioning factors in the development of the landslide predictive model was quantified using the linear support vector machine model.The receiver operating characteristic(ROC)curve(AUC),the root mean square error,and six statistical indices were used to test and compare the three resultant models.For the training dataset,the AUC values of the CNN-COA,CNN-BWO and CNN models were 0.946,0.937 and 0.855,respectively.In terms of the validation dataset,the CNN-COA model exhibited a higher AUC value of 0.919,while the AUC values of the CNN-BWO and CNN models were 0.906 and 0.805,respectively.The results indicate that the CNN-COA model,followed by the CNN-BWO model,and the CNN model,offers the best overall performance for landslide susceptibility analysis.

A High-similarity shellfish recognition method based on convolutional neural network

The high similarity of shellfish images and unbalanced samples are key factors affecting the accuracy of shellfish recognition.This study proposes a new shellfish recognition method FL_Net based on a Convolutional Neural Network(CNN).We first establish the shellfish image(SI)dataset with 68 species and 93574 images,and then propose a filter pruning and repairing model driven by an output entropy and orthogonality measurement for the recognition of shellfish with high similarity features to improve the feature expression ability of valid information.For the shellfish recognition with unbalanced samples,a hybrid loss function,including regularization term and focus loss term,is employed to reduce the weight of easily classified samples by controlling the shared weight of each sample species to the total loss.The experimental results show that the accuracy of shell-fish recognition of the proposed method is 93.95%,13.68%higher than the benchmark network(VGG16),and the accuracy of shellfish recognition is improved by 0.46%,17.41%,17.36%,4.46%,1.67%,and 1.03%respectively compared with AlexNet,GoogLeNet,ResNet50,SN_Net,MutualNet,and ResNeSt,which are used to verify the efficiency of the proposed method.