Developing an accurate and efficient comprehensive water quality prediction model and its assessment method is crucial for the prevention and control of water pollution.Deep learning(DL),as one of the most promising t...Developing an accurate and efficient comprehensive water quality prediction model and its assessment method is crucial for the prevention and control of water pollution.Deep learning(DL),as one of the most promising technologies today,plays a crucial role in the effective assessment of water body health,which is essential for water resource management.This study models using both the original dataset and a dataset augmented with Generative Adversarial Networks(GAN).It integrates optimization algorithms(OA)with Convolutional Neural Networks(CNN)to propose a comprehensive water quality model evaluation method aiming at identifying the optimal models for different pollutants.Specifically,after preprocessing the spectral dataset,data augmentation was conducted to obtain two datasets.Then,six new models were developed on these datasets using particle swarm optimization(PSO),genetic algorithm(GA),and simulated annealing(SA)combined with CNN to simulate and forecast the concentrations of three water pollutants:Chemical Oxygen Demand(COD),Total Nitrogen(TN),and Total Phosphorus(TP).Finally,seven model evaluation methods,including uncertainty analysis,were used to evaluate the constructed models and select the optimal models for the three pollutants.The evaluation results indicate that the GPSCNN model performed best in predicting COD and TP concentrations,while the GGACNN model excelled in TN concentration prediction.Compared to existing technologies,the proposed models and evaluation methods provide a more comprehensive and rapid approach to water body prediction and assessment,offering new insights and methods for water pollution prevention and control.展开更多
The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and hist...The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and historical context,DL offers a powerful complement by enhancing the speed,objectivity,and precision of the classification process.This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks(CNNs)for geological image analysis,particularly in the classification of igneous,metamorphic,and sedimentary rock types from rock thin section(RTS)images.This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision.Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities,achieving an F1-Score of 0.9869 for igneous rocks,0.9884 for metamorphic rocks,and 0.9929 for sedimentary rocks,representing improvements compared to the baseline original results.Moreover,the weighted average F1-Score across all classes and techniques is 0.9886,indicating an enhancement.Conversely,methods like Distort lead to decreased accuracy and F1-Score,with an F1-Score of 0.949 for igneous rocks,0.954 for metamorphic rocks,and 0.9416 for sedimentary rocks,exacerbating the performance compared to the baseline.The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results.The findings of this study can benefit various fields,including remote sensing,mineral exploration,and environmental monitoring,by enhancing the accuracy of geological image analysis both for scientific research and industrial applications.展开更多
Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with region...Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with regional-scale geotechnical parameters.To explore rainfall-induced LSM,this study proposes a hybrid model that combines the physically-based probabilistic model(PPM)with convolutional neural network(CNN).The PPM is capable of effectively capturing the spatial distribution of landslides by incorporating the probability of failure(POF)considering the slope stability mechanism under rainfall conditions.This significantly characterizes the variation of POF caused by parameter uncertainties.CNN was used as a binary classifier to capture the spatial and channel correlation between landslide conditioning factors and the probability of landslide occurrence.OpenCV image enhancement technique was utilized to extract non-landslide points based on the POF of landslides.The proposed model comprehensively considers physical mechanics when selecting non-landslide samples,effectively filtering out samples that do not adhere to physical principles and reduce the risk of overfitting.The results indicate that the proposed PPM-CNN hybrid model presents a higher prediction accuracy,with an area under the curve(AUC)value of 0.85 based on the landslide case of the Niangniangba area of Gansu Province,China compared with the individual CNN model(AUC=0.61)and the PPM(AUC=0.74).This model can also consider the statistical correlation and non-normal probability distributions of model parameters.These results offer practical guidance for future research on rainfall-induced LSM at the regional scale.展开更多
The effective and timely diagnosis and treatment of ocular diseases are key to the rapid recovery of patients.Today,the mass disease that needs attention in this context is cataracts.Although deep learning has signifi...The effective and timely diagnosis and treatment of ocular diseases are key to the rapid recovery of patients.Today,the mass disease that needs attention in this context is cataracts.Although deep learning has significantly advanced the analysis of ocular disease images,there is a need for a probabilistic model to generate the distributions of potential outcomes and thusmake decisions related to uncertainty quantification.Therefore,this study implements a Bayesian Convolutional Neural Networks(BCNN)model for predicting cataracts by assigning probability values to the predictions.It prepares convolutional neural network(CNN)and BCNN models.The proposed BCNN model is CNN-based in which reparameterization is in the first and last layers of the CNN model.This study then trains them on a dataset of cataract images filtered from the ocular disease fundus images fromKaggle.The deep CNN model has an accuracy of 95%,while the BCNN model has an accuracy of 93.75% along with information on uncertainty estimation of cataracts and normal eye conditions.When compared with other methods,the proposed work reveals that it can be a promising solution for cataract prediction with uncertainty estimation.展开更多
Hemoglobin is a vital protein in red blood cells responsible for transporting oxygen throughout the body.Its accurate measurement is crucial for diagnosing and managing conditions such as anemia and diabetes,where abn...Hemoglobin is a vital protein in red blood cells responsible for transporting oxygen throughout the body.Its accurate measurement is crucial for diagnosing and managing conditions such as anemia and diabetes,where abnormal hemoglobin levels can indicate significant health issues.Traditional methods for hemoglobin measurement are invasive,causing pain,risk of infection,and are less convenient for frequent monitoring.PPG is a transformative technology in wearable healthcare for noninvasive monitoring and widely explored for blood pressure,sleep,blood glucose,and stress analysis.In this work,we propose a hemoglobin estimation method using an adaptive lightweight convolutional neural network(HMALCNN)from PPG.The HMALCNN is designed to capture both fine-grained local waveform characteristics and global contextual patterns,ensuring robust performance across acquisition settings.We validated our approach on two multi-regional datasets containing 152 and 68 subjects,respectively,employing a subjectindependent 5-fold cross-validation strategy.The proposed method achieved root mean square errors(RMSE)of 0.90 and 1.20 g/dL for the two datasets,with strong Pearson correlations of 0.82 and 0.72.We conducted extensive posthoc analyses to assess clinical utility and interpretability.A±1 g/dL clinical error tolerance evaluation revealed that 91.3%and 86.7%of predictions for the two datasets fell within the acceptable clinical range.Hemoglobin range-wise analysis demonstrated consistently high accuracy in the normal and low hemoglobin categories.Statistical significance testing using the Wilcoxon signed-rank test confirmed the stability of performance across validation folds(p>0.05 for both RMSE and correlation).Furthermore,model interpretability was enhanced using Gradient-weighted Class Activation Mapping(Grad-CAM),supporting the model’s clinical trustworthiness.The proposed HMALCNN offers a computationally efficient,clinically interpretable,and generalizable framework for noninvasive hemoglobin monitoring,with strong potential for integration into wearable healthcare systems as a practical alternative to invasive measurement techniques.展开更多
To enhance the inference efficiency of convolutional neural network(CNN),tensor parallelism is employed to improve the parallelism within operators.However,existing methods are customized to specific networks and hard...To enhance the inference efficiency of convolutional neural network(CNN),tensor parallelism is employed to improve the parallelism within operators.However,existing methods are customized to specific networks and hardware,limiting their generalizability.This paper proposes an approach called resource-adaptive tensor decomposition(RATD)for CNN operators,which aims to achieve an optimal match between computational resources and parallel computing tasks.Firstly,CNN is represented with fine-grained tensors at the lower graph level,thereby decoupling tensors that can be computed in parallel within operators.Secondly,the convolution and pooling operators are fused,and the decoupled tensor blocks are scheduled in parallel.Finally,a cost model is constructed,based on runtime and resource utilization,to iteratively refine the process of tensor block decomposition and automatically determine the optimal tensor decomposition.Experimental results demonstrate that the proposed RATD improves the accuracy of the model by 11%.Compared with CUDA(compute unified device architecture)deep neural network library(cuDNN),RATD achieves an average speedup ratio of 1.21 times in inference time across various convolution kernels,along with a 12%increase in computational resource utilization.展开更多
Even though several advances have been made in recent years,handwritten script recognition is still a challenging task in the pattern recognition domain.This field has gained much interest lately due to its diverse ap...Even though several advances have been made in recent years,handwritten script recognition is still a challenging task in the pattern recognition domain.This field has gained much interest lately due to its diverse application potentials.Nowadays,different methods are available for automatic script recognition.Among most of the reported script recognition techniques,deep neural networks have achieved impressive results and outperformed the classical machine learning algorithms.However,the process of designing such networks right from scratch intuitively appears to incur a significant amount of trial and error,which renders them unfeasible.This approach often requires manual intervention with domain expertise which consumes substantial time and computational resources.To alleviate this shortcoming,this paper proposes a new neural architecture search approach based on meta-heuristic quantum particle swarm optimization(QPSO),which is capable of automatically evolving the meaningful convolutional neural network(CNN)topologies.The computational experiments have been conducted on eight different datasets belonging to three popular Indic scripts,namely Bangla,Devanagari,and Dogri,consisting of handwritten characters and digits.Empirically,the results imply that the proposed QPSO-CNN algorithm outperforms the classical and state-of-the-art methods with faster prediction and higher accuracy.展开更多
This paper concerns the problem of object segmentation in real-time for picking system. A region proposal method inspired by human glance based on the convolutional neural network is proposed to select promising regio...This paper concerns the problem of object segmentation in real-time for picking system. A region proposal method inspired by human glance based on the convolutional neural network is proposed to select promising regions, allowing more processing is reserved only for these regions. The speed of object segmentation is significantly improved by the region proposal method.By the combination of the region proposal method based on the convolutional neural network and superpixel method, the category and location information can be used to segment objects and image redundancy is significantly reduced. The processing time is reduced considerably by this to achieve the real time. Experiments show that the proposed method can segment the interested target object in real time on an ordinary laptop.展开更多
The recent surge of mobile subscribers and user data traffic has accelerated the telecommunication sector towards the adoption of the fifth-generation (5G) mobile networks. Cloud radio access network (CRAN) is a promi...The recent surge of mobile subscribers and user data traffic has accelerated the telecommunication sector towards the adoption of the fifth-generation (5G) mobile networks. Cloud radio access network (CRAN) is a prominent framework in the 5G mobile network to meet the above requirements by deploying low-cost and intelligent multiple distributed antennas known as remote radio heads (RRHs). However, achieving the optimal resource allocation (RA) in CRAN using the traditional approach is still challenging due to the complex structure. In this paper, we introduce the convolutional neural network-based deep Q-network (CNN-DQN) to balance the energy consumption and guarantee the user quality of service (QoS) demand in downlink CRAN. We first formulate the Markov decision process (MDP) for energy efficiency (EE) and build up a 3-layer CNN to capture the environment feature as an input state space. We then use DQN to turn on/off the RRHs dynamically based on the user QoS demand and energy consumption in the CRAN. Finally, we solve the RA problem based on the user constraint and transmit power to guarantee the user QoS demand and maximize the EE with a minimum number of active RRHs. In the end, we conduct the simulation to compare our proposed scheme with nature DQN and the traditional approach.展开更多
In airborne gamma ray spectrum processing,different analysis methods,technical requirements,analysis models,and calculation methods need to be established.To meet the engineering practice requirements of airborne gamm...In airborne gamma ray spectrum processing,different analysis methods,technical requirements,analysis models,and calculation methods need to be established.To meet the engineering practice requirements of airborne gamma-ray measurements and improve computational efficiency,an improved shuffled frog leaping algorithm-particle swarm optimization convolutional neural network(SFLA-PSO CNN)for large-sample quantitative analysis of airborne gamma-ray spectra is proposed herein.This method was used to train the weight of the neural network,optimize the structure of the network,delete redundant connections,and enable the neural network to acquire the capability of quantitative spectrum processing.In full-spectrum data processing,this method can perform the functions of energy spectrum peak searching and peak area calculations.After network training,the mean SNR and RMSE of the spectral lines were 31.27 and 2.75,respectively,satisfying the demand for noise reduction.To test the processing ability of the algorithm in large samples of airborne gamma spectra,this study considered the measured data from the Saihangaobi survey area as an example to conduct data spectral analysis.The results show that calculation of the single-peak area takes only 0.13~0.15 ms,and the average relative errors of the peak area in the U,Th,and K spectra are 3.11,9.50,and 6.18%,indicating the high processing efficiency and accuracy of this algorithm.The performance of the model can be further improved by optimizing related parameters,but it can already meet the requirements of practical engineering measurement.This study provides a new idea for the full-spectrum processing of airborne gamma rays.展开更多
This study introduces an innovative“Big Model”strategy to enhance Bridge Structural Health Monitoring(SHM)using a Convolutional Neural Network(CNN),time-frequency analysis,and fine element analysis.Leveraging ensemb...This study introduces an innovative“Big Model”strategy to enhance Bridge Structural Health Monitoring(SHM)using a Convolutional Neural Network(CNN),time-frequency analysis,and fine element analysis.Leveraging ensemble methods,collaborative learning,and distributed computing,the approach effectively manages the complexity and scale of large-scale bridge data.The CNN employs transfer learning,fine-tuning,and continuous monitoring to optimize models for adaptive and accurate structural health assessments,focusing on extracting meaningful features through time-frequency analysis.By integrating Finite Element Analysis,time-frequency analysis,and CNNs,the strategy provides a comprehensive understanding of bridge health.Utilizing diverse sensor data,sophisticated feature extraction,and advanced CNN architecture,the model is optimized through rigorous preprocessing and hyperparameter tuning.This approach significantly enhances the ability to make accurate predictions,monitor structural health,and support proactive maintenance practices,thereby ensuring the safety and longevity of critical infrastructure.展开更多
COVID-19 disease caused by the SARS-CoV-2 virus has created social and economic disruption across theworld.The ability of the COVID-19 virus to quickly mutate and transfer has created serious concerns across the world...COVID-19 disease caused by the SARS-CoV-2 virus has created social and economic disruption across theworld.The ability of the COVID-19 virus to quickly mutate and transfer has created serious concerns across the world.It is essential to detectCOVID-19 infection caused by different variants to take preventive measures accordingly.The existing method of detection of infections caused by COVID-19 and its variants is costly and time-consuming.The impacts of theCOVID-19 pandemic in developing countries are very drastic due to the unavailability of medical facilities and infrastructure to handle the pandemic.Pneumonia is the major symptom of COVID-19 infection.The radiology of the lungs in varies in the case of bacterial pneumonia as compared to COVID-19-caused pneumonia.The pattern of pneumonia in lungs in radiology images can also be used to identify the cause associated with pneumonia.In this paper,we propose the methodology of identifying the cause(either due to COVID-19 or other types of infections)of pneumonia from radiology images.Furthermore,because different variants of COVID-19 lead to different patterns of pneumonia,the proposed methodology identifies pneumonia,the COVID-19 caused pneumonia,and Omicron caused pneumonia from the radiology images.To fulfill the above-mentioned tasks,we have used three Convolution Neural Networks(CNNs)at each stage of the proposed methodology.The results unveil that the proposed step-by-step solution enhances the accuracy of pneumonia detection along with finding its cause,despite having a limited dataset.展开更多
In recent years,there has been significant research on the application of deep learning(DL)in topology optimization(TO)to accelerate structural design.However,these methods have primarily focused on solving binary TO ...In recent years,there has been significant research on the application of deep learning(DL)in topology optimization(TO)to accelerate structural design.However,these methods have primarily focused on solving binary TO problems,and effective solutions for multi-material topology optimization(MMTO)which requires a lot of computing resources are still lacking.Therefore,this paper proposes the framework of multiphase topology optimization using deep learning to accelerate MMTO design.The framework employs convolutional neural network(CNN)to construct a surrogate model for solving MMTO,and the obtained surrogate model can rapidly generate multi-material structure topologies in negligible time without any iterations.The performance evaluation results show that the proposed method not only outputs multi-material topologies with clear material boundary but also reduces the calculation cost with high prediction accuracy.Additionally,in order to find a more reasonable modeling method for MMTO,this paper studies the characteristics of surrogate modeling as regression task and classification task.Through the training of 297 models,our findings show that the regression task yields slightly better results than the classification task in most cases.Furthermore,The results indicate that the prediction accuracy is primarily influenced by factors such as the TO problem,material category,and data scale.Conversely,factors such as the domain size and the material property have minimal impact on the accuracy.展开更多
Diabetes mellitus is a long-term condition characterized by hyperglycemia.It could lead to plenty of difficulties.According to rising morbidity in recent years,the world’s diabetic patients will exceed 642 million by...Diabetes mellitus is a long-term condition characterized by hyperglycemia.It could lead to plenty of difficulties.According to rising morbidity in recent years,the world’s diabetic patients will exceed 642 million by 2040,implying that one out of every ten persons will be diabetic.There is no doubt that this startling figure requires immediate attention from industry and academia to promote innovation and growth in diabetes risk prediction to save individuals’lives.Due to its rapid development,deep learning(DL)was used to predict numerous diseases.However,DLmethods still suffer from their limited prediction performance due to the hyperparameters selection and parameters optimization.Therefore,the selection of hyper-parameters is critical in improving classification performance.This study presents Convolutional Neural Network(CNN)that has achieved remarkable results in many medical domains where the Bayesian optimization algorithm(BOA)has been employed for hyperparameters selection and parameters optimization.Two issues have been investigated and solved during the experiment to enhance the results.The first is the dataset class imbalance,which is solved using Synthetic Minority Oversampling Technique(SMOTE)technique.The second issue is the model’s poor performance,which has been solved using the Bayesian optimization algorithm.The findings indicate that the Bayesian based-CNN model superbases all the state-of-the-art models in the literature with an accuracy of 89.36%,F1-score of 0.88.6,andMatthews Correlation Coefficient(MCC)of 0.88.6.展开更多
In computer vision,convolutional neural networks have a wide range of uses.Images representmost of today’s data,so it’s important to know how to handle these large amounts of data efficiently.Convolutional neural ne...In computer vision,convolutional neural networks have a wide range of uses.Images representmost of today’s data,so it’s important to know how to handle these large amounts of data efficiently.Convolutional neural networks have been shown to solve image processing problems effectively.However,when designing the network structure for a particular problem,you need to adjust the hyperparameters for higher accuracy.This technique is time consuming and requires a lot of work and domain knowledge.Designing a convolutional neural network architecture is a classic NP-hard optimization challenge.On the other hand,different datasets require different combinations of models or hyperparameters,which can be time consuming and inconvenient.Various approaches have been proposed to overcome this problem,such as grid search limited to low-dimensional space and queuing by random selection.To address this issue,we propose an evolutionary algorithm-based approach that dynamically enhances the structure of Convolution Neural Networks(CNNs)using optimized hyperparameters.This study proposes a method using Non-dominated sorted genetic algorithms(NSGA)to improve the hyperparameters of the CNN model.In addition,different types and parameter ranges of existing genetic algorithms are used.Acomparative study was conducted with various state-of-the-art methodologies and algorithms.Experiments have shown that our proposed approach is superior to previous methods in terms of classification accuracy,and the results are published in modern computing literature.展开更多
The surge in connected devices and massive data aggregation has expanded the scale of the Internet of Things(IoT)networks.The proliferation of unknown attacks and related risks,such as zero-day attacks and Distributed...The surge in connected devices and massive data aggregation has expanded the scale of the Internet of Things(IoT)networks.The proliferation of unknown attacks and related risks,such as zero-day attacks and Distributed Denial of Service(DDoS)attacks triggered by botnets,have resulted in information leakage and property damage.Therefore,developing an efficient and realistic intrusion detection system(IDS)is critical for ensuring IoT network security.In recent years,traditional machine learning techniques have struggled to learn the complex associations between multidimensional features in network traffic,and the excellent performance of deep learning techniques,as an advanced version of machine learning,has led to their widespread application in intrusion detection.In this paper,we propose an Adaptive Particle Swarm Optimization Convolutional Neural Network Squeeze-andExcitation(APSO-CNN-SE)model for implementing IoT network intrusion detection.A 2D CNN backbone is initially constructed to extract spatial features from network traffic.Subsequently,a squeeze-and-excitation channel attention mechanism is introduced and embedded into the CNN to focus on critical feature channels.Lastly,the weights and biases in the CNN-SE are extracted to initialize the population individuals of the APSO.As the number of iterations increases,the population’s position vector is continuously updated,and the cross-entropy loss function value is minimized to produce the ideal network architecture.We evaluated the models experimentally using binary and multiclassification on the UNSW-NB15 and NSL-KDD datasets,comparing and analyzing the evaluation metrics derived from each model.Compared to the base CNN model,the results demonstrate that APSO-CNNSE enhances the binary classification detection accuracy by 1.84%and 3.53%and the multiclassification detection accuracy by 1.56%and 2.73%on the two datasets,respectively.Additionally,the model outperforms the existing models like DT,KNN,LR,SVM,LSTM,etc.,in terms of accuracy and fitting performance.This means that the model can identify potential attacks or anomalies more precisely,improving the overall security and stability of the IoT environment.展开更多
With the development of the economy and the surge in car ownership, the sale of used cars has been welcomed by more and more people, and the information of the vehicle condition is the focus information of them. The f...With the development of the economy and the surge in car ownership, the sale of used cars has been welcomed by more and more people, and the information of the vehicle condition is the focus information of them. The frame number is a unique number used in the vehicle, and by identifying it can quickly find out the vehicle models and manufacturers. The traditional character recognition method has the problem of complex feature extraction, and the convolutional neural network has unique advantages in processing two-dimensional images. This paper analyzed the key techniques of convolutional neural networks compared with traditional neural networks, and proposed improved methods for key technologies, thus increasing the recognition of characters and applying them to the recognition of frame number characters.展开更多
With the high-speed development of the Internet,a growing number of Internet users like giving their subjective comments in the BBS,blog and shopping website.These comments contains critics’attitudes,emotions,views a...With the high-speed development of the Internet,a growing number of Internet users like giving their subjective comments in the BBS,blog and shopping website.These comments contains critics’attitudes,emotions,views and other information.Using these information reasonablely can help understand the social public opinion and make a timely response and help dealer to improve quality and service of products and make consumers know merchandise.This paper mainly discusses using convolutional neural network(CNN)for the operation of the text feature extraction.The concrete realization are discussed.Then combining with other text classifier make class operation.The experiment result shows the effectiveness of the method which is proposed in this paper.展开更多
A demodulator based on convolutional neural networks（ CNNs） is proposed to demodulate bipolar extended binary phase shifting keying（ EBPSK） signals transmitted at a faster-thanNyquist（ FTN） rate, solving the pro...A demodulator based on convolutional neural networks（ CNNs） is proposed to demodulate bipolar extended binary phase shifting keying（ EBPSK） signals transmitted at a faster-thanNyquist（ FTN） rate, solving the problem of severe inter symbol interference（ ISI） caused by FTN rate signals. With the characteristics of local connectivity, pooling and weight sharing,a six-layer CNNs structure is used to demodulate and eliminate ISI. The results showthat with the symbol rate of 1. 07 k Bd, the bandwidth of the band-pass filter（ BPF） in a transmitter of 1 k Hz and the changing number of carrier cycles in a symbol K = 5,10,15,28, the overall bit error ratio（ BER） performance of CNNs with single-symbol decision is superior to that with a doublesymbol united-decision. In addition, the BER performance of single-symbol decision is approximately 0. 5 d B better than that of the coherent demodulator while K equals the total number of carrier circles in a symbol, i. e., K = N = 28. With the symbol rate of 1. 07 k Bd, the bandwidth of BPF in a transmitter of 500 Hz and K = 5,10,15,28, the overall BER performance of CNNs with double-symbol united-decision is superior to those with single-symbol decision. Moreover, the double-symbol uniteddecision method is approximately 0. 5 to 1. 5 d B better than that of the coherent demodulator while K = N = 28. The demodulators based on CNNs successfully solve the serious ISI problems generated during the transmission of FTN rate bipolar EBPSK signals, which is beneficial for the improvement of spectrum efficiency.展开更多
Nowadays,the amount of wed data is increasing at a rapid speed,which presents a serious challenge to the web monitoring.Text sentiment analysis,an important research topic in the area of natural language processing,is...Nowadays,the amount of wed data is increasing at a rapid speed,which presents a serious challenge to the web monitoring.Text sentiment analysis,an important research topic in the area of natural language processing,is a crucial task in the web monitoring area.The accuracy of traditional text sentiment analysis methods might be degraded in dealing with mass data.Deep learning is a hot research topic of the artificial intelligence in the recent years.By now,several research groups have studied the sentiment analysis of English texts using deep learning methods.In contrary,relatively few works have so far considered the Chinese text sentiment analysis toward this direction.In this paper,a method for analyzing the Chinese text sentiment is proposed based on the convolutional neural network(CNN)in deep learning in order to improve the analysis accuracy.The feature values of the CNN after the training process are nonuniformly distributed.In order to overcome this problem,a method for normalizing the feature values is proposed.Moreover,the dimensions of the text features are optimized through simulations.Finally,a method for updating the learning rate in the training process of the CNN is presented in order to achieve better performances.Experiment results on the typical datasets indicate that the accuracy of the proposed method can be improved compared with that of the traditional supervised machine learning methods,e.g.,the support vector machine method.展开更多
基金Supported by Natural Science Basic Research Plan in Shaanxi Province of China(Program No.2022JM-396)the Strategic Priority Research Program of the Chinese Academy of Sciences,Grant No.XDA23040101+4 种基金Shaanxi Province Key Research and Development Projects(Program No.2023-YBSF-437)Xi'an Shiyou University Graduate Student Innovation Fund Program(Program No.YCX2412041)State Key Laboratory of Air Traffic Management System and Technology(SKLATM202001)Tianjin Education Commission Research Program Project(2020KJ028)Fundamental Research Funds for the Central Universities(3122019132)。
文摘Developing an accurate and efficient comprehensive water quality prediction model and its assessment method is crucial for the prevention and control of water pollution.Deep learning(DL),as one of the most promising technologies today,plays a crucial role in the effective assessment of water body health,which is essential for water resource management.This study models using both the original dataset and a dataset augmented with Generative Adversarial Networks(GAN).It integrates optimization algorithms(OA)with Convolutional Neural Networks(CNN)to propose a comprehensive water quality model evaluation method aiming at identifying the optimal models for different pollutants.Specifically,after preprocessing the spectral dataset,data augmentation was conducted to obtain two datasets.Then,six new models were developed on these datasets using particle swarm optimization(PSO),genetic algorithm(GA),and simulated annealing(SA)combined with CNN to simulate and forecast the concentrations of three water pollutants:Chemical Oxygen Demand(COD),Total Nitrogen(TN),and Total Phosphorus(TP).Finally,seven model evaluation methods,including uncertainty analysis,were used to evaluate the constructed models and select the optimal models for the three pollutants.The evaluation results indicate that the GPSCNN model performed best in predicting COD and TP concentrations,while the GGACNN model excelled in TN concentration prediction.Compared to existing technologies,the proposed models and evaluation methods provide a more comprehensive and rapid approach to water body prediction and assessment,offering new insights and methods for water pollution prevention and control.
文摘The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and historical context,DL offers a powerful complement by enhancing the speed,objectivity,and precision of the classification process.This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks(CNNs)for geological image analysis,particularly in the classification of igneous,metamorphic,and sedimentary rock types from rock thin section(RTS)images.This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision.Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities,achieving an F1-Score of 0.9869 for igneous rocks,0.9884 for metamorphic rocks,and 0.9929 for sedimentary rocks,representing improvements compared to the baseline original results.Moreover,the weighted average F1-Score across all classes and techniques is 0.9886,indicating an enhancement.Conversely,methods like Distort lead to decreased accuracy and F1-Score,with an F1-Score of 0.949 for igneous rocks,0.954 for metamorphic rocks,and 0.9416 for sedimentary rocks,exacerbating the performance compared to the baseline.The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results.The findings of this study can benefit various fields,including remote sensing,mineral exploration,and environmental monitoring,by enhancing the accuracy of geological image analysis both for scientific research and industrial applications.
基金funding support from the National Natural Science Foundation of China(Grant Nos.U22A20594,52079045)Hong-Zhi Cui acknowledges the financial support of the China Scholarship Council(Grant No.CSC:202206710014)for his research at Universitat Politecnica de Catalunya,Barcelona.
文摘Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with regional-scale geotechnical parameters.To explore rainfall-induced LSM,this study proposes a hybrid model that combines the physically-based probabilistic model(PPM)with convolutional neural network(CNN).The PPM is capable of effectively capturing the spatial distribution of landslides by incorporating the probability of failure(POF)considering the slope stability mechanism under rainfall conditions.This significantly characterizes the variation of POF caused by parameter uncertainties.CNN was used as a binary classifier to capture the spatial and channel correlation between landslide conditioning factors and the probability of landslide occurrence.OpenCV image enhancement technique was utilized to extract non-landslide points based on the POF of landslides.The proposed model comprehensively considers physical mechanics when selecting non-landslide samples,effectively filtering out samples that do not adhere to physical principles and reduce the risk of overfitting.The results indicate that the proposed PPM-CNN hybrid model presents a higher prediction accuracy,with an area under the curve(AUC)value of 0.85 based on the landslide case of the Niangniangba area of Gansu Province,China compared with the individual CNN model(AUC=0.61)and the PPM(AUC=0.74).This model can also consider the statistical correlation and non-normal probability distributions of model parameters.These results offer practical guidance for future research on rainfall-induced LSM at the regional scale.
基金Saudi Arabia for funding this work through Small Research Group Project under Grant Number RGP.1/316/45.
文摘The effective and timely diagnosis and treatment of ocular diseases are key to the rapid recovery of patients.Today,the mass disease that needs attention in this context is cataracts.Although deep learning has significantly advanced the analysis of ocular disease images,there is a need for a probabilistic model to generate the distributions of potential outcomes and thusmake decisions related to uncertainty quantification.Therefore,this study implements a Bayesian Convolutional Neural Networks(BCNN)model for predicting cataracts by assigning probability values to the predictions.It prepares convolutional neural network(CNN)and BCNN models.The proposed BCNN model is CNN-based in which reparameterization is in the first and last layers of the CNN model.This study then trains them on a dataset of cataract images filtered from the ocular disease fundus images fromKaggle.The deep CNN model has an accuracy of 95%,while the BCNN model has an accuracy of 93.75% along with information on uncertainty estimation of cataracts and normal eye conditions.When compared with other methods,the proposed work reveals that it can be a promising solution for cataract prediction with uncertainty estimation.
基金funded by the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2025).
文摘Hemoglobin is a vital protein in red blood cells responsible for transporting oxygen throughout the body.Its accurate measurement is crucial for diagnosing and managing conditions such as anemia and diabetes,where abnormal hemoglobin levels can indicate significant health issues.Traditional methods for hemoglobin measurement are invasive,causing pain,risk of infection,and are less convenient for frequent monitoring.PPG is a transformative technology in wearable healthcare for noninvasive monitoring and widely explored for blood pressure,sleep,blood glucose,and stress analysis.In this work,we propose a hemoglobin estimation method using an adaptive lightweight convolutional neural network(HMALCNN)from PPG.The HMALCNN is designed to capture both fine-grained local waveform characteristics and global contextual patterns,ensuring robust performance across acquisition settings.We validated our approach on two multi-regional datasets containing 152 and 68 subjects,respectively,employing a subjectindependent 5-fold cross-validation strategy.The proposed method achieved root mean square errors(RMSE)of 0.90 and 1.20 g/dL for the two datasets,with strong Pearson correlations of 0.82 and 0.72.We conducted extensive posthoc analyses to assess clinical utility and interpretability.A±1 g/dL clinical error tolerance evaluation revealed that 91.3%and 86.7%of predictions for the two datasets fell within the acceptable clinical range.Hemoglobin range-wise analysis demonstrated consistently high accuracy in the normal and low hemoglobin categories.Statistical significance testing using the Wilcoxon signed-rank test confirmed the stability of performance across validation folds(p>0.05 for both RMSE and correlation).Furthermore,model interpretability was enhanced using Gradient-weighted Class Activation Mapping(Grad-CAM),supporting the model’s clinical trustworthiness.The proposed HMALCNN offers a computationally efficient,clinically interpretable,and generalizable framework for noninvasive hemoglobin monitoring,with strong potential for integration into wearable healthcare systems as a practical alternative to invasive measurement techniques.
基金Supported by the National Science and Technology Major Project of China(No.2022ZD0119003)the National Natural Science Foundation of China(No.61834005).
文摘To enhance the inference efficiency of convolutional neural network(CNN),tensor parallelism is employed to improve the parallelism within operators.However,existing methods are customized to specific networks and hardware,limiting their generalizability.This paper proposes an approach called resource-adaptive tensor decomposition(RATD)for CNN operators,which aims to achieve an optimal match between computational resources and parallel computing tasks.Firstly,CNN is represented with fine-grained tensors at the lower graph level,thereby decoupling tensors that can be computed in parallel within operators.Secondly,the convolution and pooling operators are fused,and the decoupled tensor blocks are scheduled in parallel.Finally,a cost model is constructed,based on runtime and resource utilization,to iteratively refine the process of tensor block decomposition and automatically determine the optimal tensor decomposition.Experimental results demonstrate that the proposed RATD improves the accuracy of the model by 11%.Compared with CUDA(compute unified device architecture)deep neural network library(cuDNN),RATD achieves an average speedup ratio of 1.21 times in inference time across various convolution kernels,along with a 12%increase in computational resource utilization.
文摘Even though several advances have been made in recent years,handwritten script recognition is still a challenging task in the pattern recognition domain.This field has gained much interest lately due to its diverse application potentials.Nowadays,different methods are available for automatic script recognition.Among most of the reported script recognition techniques,deep neural networks have achieved impressive results and outperformed the classical machine learning algorithms.However,the process of designing such networks right from scratch intuitively appears to incur a significant amount of trial and error,which renders them unfeasible.This approach often requires manual intervention with domain expertise which consumes substantial time and computational resources.To alleviate this shortcoming,this paper proposes a new neural architecture search approach based on meta-heuristic quantum particle swarm optimization(QPSO),which is capable of automatically evolving the meaningful convolutional neural network(CNN)topologies.The computational experiments have been conducted on eight different datasets belonging to three popular Indic scripts,namely Bangla,Devanagari,and Dogri,consisting of handwritten characters and digits.Empirically,the results imply that the proposed QPSO-CNN algorithm outperforms the classical and state-of-the-art methods with faster prediction and higher accuracy.
基金supported by the National Natural Science Foundation of China(61233010 61305106)+2 种基金the Shanghai Natural Science Foundation(17ZR1409700 18ZR1415300)the basic research project of Shanghai Municipal Science and Technology Commission(16JC1400900)
文摘This paper concerns the problem of object segmentation in real-time for picking system. A region proposal method inspired by human glance based on the convolutional neural network is proposed to select promising regions, allowing more processing is reserved only for these regions. The speed of object segmentation is significantly improved by the region proposal method.By the combination of the region proposal method based on the convolutional neural network and superpixel method, the category and location information can be used to segment objects and image redundancy is significantly reduced. The processing time is reduced considerably by this to achieve the real time. Experiments show that the proposed method can segment the interested target object in real time on an ordinary laptop.
基金supported by the Universiti Tunku Abdul Rahman (UTAR) Malaysia under UTARRF (IPSR/RMC/UTARRF/2021-C1/T05)
文摘The recent surge of mobile subscribers and user data traffic has accelerated the telecommunication sector towards the adoption of the fifth-generation (5G) mobile networks. Cloud radio access network (CRAN) is a prominent framework in the 5G mobile network to meet the above requirements by deploying low-cost and intelligent multiple distributed antennas known as remote radio heads (RRHs). However, achieving the optimal resource allocation (RA) in CRAN using the traditional approach is still challenging due to the complex structure. In this paper, we introduce the convolutional neural network-based deep Q-network (CNN-DQN) to balance the energy consumption and guarantee the user quality of service (QoS) demand in downlink CRAN. We first formulate the Markov decision process (MDP) for energy efficiency (EE) and build up a 3-layer CNN to capture the environment feature as an input state space. We then use DQN to turn on/off the RRHs dynamically based on the user QoS demand and energy consumption in the CRAN. Finally, we solve the RA problem based on the user constraint and transmit power to guarantee the user QoS demand and maximize the EE with a minimum number of active RRHs. In the end, we conduct the simulation to compare our proposed scheme with nature DQN and the traditional approach.
基金the National Natural Science Foundation of China(No.42127807)Natural Science Foundation of Sichuan Province(Nos.23NSFSCC0116 and 2022NSFSC12333)the Nuclear Energy Development Project(No.[2021]-88).
文摘In airborne gamma ray spectrum processing,different analysis methods,technical requirements,analysis models,and calculation methods need to be established.To meet the engineering practice requirements of airborne gamma-ray measurements and improve computational efficiency,an improved shuffled frog leaping algorithm-particle swarm optimization convolutional neural network(SFLA-PSO CNN)for large-sample quantitative analysis of airborne gamma-ray spectra is proposed herein.This method was used to train the weight of the neural network,optimize the structure of the network,delete redundant connections,and enable the neural network to acquire the capability of quantitative spectrum processing.In full-spectrum data processing,this method can perform the functions of energy spectrum peak searching and peak area calculations.After network training,the mean SNR and RMSE of the spectral lines were 31.27 and 2.75,respectively,satisfying the demand for noise reduction.To test the processing ability of the algorithm in large samples of airborne gamma spectra,this study considered the measured data from the Saihangaobi survey area as an example to conduct data spectral analysis.The results show that calculation of the single-peak area takes only 0.13~0.15 ms,and the average relative errors of the peak area in the U,Th,and K spectra are 3.11,9.50,and 6.18%,indicating the high processing efficiency and accuracy of this algorithm.The performance of the model can be further improved by optimizing related parameters,but it can already meet the requirements of practical engineering measurement.This study provides a new idea for the full-spectrum processing of airborne gamma rays.
文摘This study introduces an innovative“Big Model”strategy to enhance Bridge Structural Health Monitoring(SHM)using a Convolutional Neural Network(CNN),time-frequency analysis,and fine element analysis.Leveraging ensemble methods,collaborative learning,and distributed computing,the approach effectively manages the complexity and scale of large-scale bridge data.The CNN employs transfer learning,fine-tuning,and continuous monitoring to optimize models for adaptive and accurate structural health assessments,focusing on extracting meaningful features through time-frequency analysis.By integrating Finite Element Analysis,time-frequency analysis,and CNNs,the strategy provides a comprehensive understanding of bridge health.Utilizing diverse sensor data,sophisticated feature extraction,and advanced CNN architecture,the model is optimized through rigorous preprocessing and hyperparameter tuning.This approach significantly enhances the ability to make accurate predictions,monitor structural health,and support proactive maintenance practices,thereby ensuring the safety and longevity of critical infrastructure.
文摘COVID-19 disease caused by the SARS-CoV-2 virus has created social and economic disruption across theworld.The ability of the COVID-19 virus to quickly mutate and transfer has created serious concerns across the world.It is essential to detectCOVID-19 infection caused by different variants to take preventive measures accordingly.The existing method of detection of infections caused by COVID-19 and its variants is costly and time-consuming.The impacts of theCOVID-19 pandemic in developing countries are very drastic due to the unavailability of medical facilities and infrastructure to handle the pandemic.Pneumonia is the major symptom of COVID-19 infection.The radiology of the lungs in varies in the case of bacterial pneumonia as compared to COVID-19-caused pneumonia.The pattern of pneumonia in lungs in radiology images can also be used to identify the cause associated with pneumonia.In this paper,we propose the methodology of identifying the cause(either due to COVID-19 or other types of infections)of pneumonia from radiology images.Furthermore,because different variants of COVID-19 lead to different patterns of pneumonia,the proposed methodology identifies pneumonia,the COVID-19 caused pneumonia,and Omicron caused pneumonia from the radiology images.To fulfill the above-mentioned tasks,we have used three Convolution Neural Networks(CNNs)at each stage of the proposed methodology.The results unveil that the proposed step-by-step solution enhances the accuracy of pneumonia detection along with finding its cause,despite having a limited dataset.
基金supported in part by National Natural Science Foundation of China under Grant Nos.51675525,52005505,and 62001502Post-Graduate Scientific Research Innovation Project of Hunan Province under Grant No.XJCX2023185.
文摘In recent years,there has been significant research on the application of deep learning(DL)in topology optimization(TO)to accelerate structural design.However,these methods have primarily focused on solving binary TO problems,and effective solutions for multi-material topology optimization(MMTO)which requires a lot of computing resources are still lacking.Therefore,this paper proposes the framework of multiphase topology optimization using deep learning to accelerate MMTO design.The framework employs convolutional neural network(CNN)to construct a surrogate model for solving MMTO,and the obtained surrogate model can rapidly generate multi-material structure topologies in negligible time without any iterations.The performance evaluation results show that the proposed method not only outputs multi-material topologies with clear material boundary but also reduces the calculation cost with high prediction accuracy.Additionally,in order to find a more reasonable modeling method for MMTO,this paper studies the characteristics of surrogate modeling as regression task and classification task.Through the training of 297 models,our findings show that the regression task yields slightly better results than the classification task in most cases.Furthermore,The results indicate that the prediction accuracy is primarily influenced by factors such as the TO problem,material category,and data scale.Conversely,factors such as the domain size and the material property have minimal impact on the accuracy.
基金This research/paper was fully supported by Universiti Teknologi PETRONAS,under the Yayasan Universiti Teknologi PETRONAS(YUTP)Fundamental Research Grant Scheme(015LC0-311).
文摘Diabetes mellitus is a long-term condition characterized by hyperglycemia.It could lead to plenty of difficulties.According to rising morbidity in recent years,the world’s diabetic patients will exceed 642 million by 2040,implying that one out of every ten persons will be diabetic.There is no doubt that this startling figure requires immediate attention from industry and academia to promote innovation and growth in diabetes risk prediction to save individuals’lives.Due to its rapid development,deep learning(DL)was used to predict numerous diseases.However,DLmethods still suffer from their limited prediction performance due to the hyperparameters selection and parameters optimization.Therefore,the selection of hyper-parameters is critical in improving classification performance.This study presents Convolutional Neural Network(CNN)that has achieved remarkable results in many medical domains where the Bayesian optimization algorithm(BOA)has been employed for hyperparameters selection and parameters optimization.Two issues have been investigated and solved during the experiment to enhance the results.The first is the dataset class imbalance,which is solved using Synthetic Minority Oversampling Technique(SMOTE)technique.The second issue is the model’s poor performance,which has been solved using the Bayesian optimization algorithm.The findings indicate that the Bayesian based-CNN model superbases all the state-of-the-art models in the literature with an accuracy of 89.36%,F1-score of 0.88.6,andMatthews Correlation Coefficient(MCC)of 0.88.6.
基金This research was supported by the Researchers Supporting Program(TUMAProject-2021-27)Almaarefa University,Riyadh,Saudi Arabia.
文摘In computer vision,convolutional neural networks have a wide range of uses.Images representmost of today’s data,so it’s important to know how to handle these large amounts of data efficiently.Convolutional neural networks have been shown to solve image processing problems effectively.However,when designing the network structure for a particular problem,you need to adjust the hyperparameters for higher accuracy.This technique is time consuming and requires a lot of work and domain knowledge.Designing a convolutional neural network architecture is a classic NP-hard optimization challenge.On the other hand,different datasets require different combinations of models or hyperparameters,which can be time consuming and inconvenient.Various approaches have been proposed to overcome this problem,such as grid search limited to low-dimensional space and queuing by random selection.To address this issue,we propose an evolutionary algorithm-based approach that dynamically enhances the structure of Convolution Neural Networks(CNNs)using optimized hyperparameters.This study proposes a method using Non-dominated sorted genetic algorithms(NSGA)to improve the hyperparameters of the CNN model.In addition,different types and parameter ranges of existing genetic algorithms are used.Acomparative study was conducted with various state-of-the-art methodologies and algorithms.Experiments have shown that our proposed approach is superior to previous methods in terms of classification accuracy,and the results are published in modern computing literature.
基金the National Natural Science Foundation of China“Research on the Evidence Chain Construction from the Analysis of the Investigation Documents(62166006)”the Natural Science Foundation of Guizhou Province under Grant[2020]1Y254.
文摘The surge in connected devices and massive data aggregation has expanded the scale of the Internet of Things(IoT)networks.The proliferation of unknown attacks and related risks,such as zero-day attacks and Distributed Denial of Service(DDoS)attacks triggered by botnets,have resulted in information leakage and property damage.Therefore,developing an efficient and realistic intrusion detection system(IDS)is critical for ensuring IoT network security.In recent years,traditional machine learning techniques have struggled to learn the complex associations between multidimensional features in network traffic,and the excellent performance of deep learning techniques,as an advanced version of machine learning,has led to their widespread application in intrusion detection.In this paper,we propose an Adaptive Particle Swarm Optimization Convolutional Neural Network Squeeze-andExcitation(APSO-CNN-SE)model for implementing IoT network intrusion detection.A 2D CNN backbone is initially constructed to extract spatial features from network traffic.Subsequently,a squeeze-and-excitation channel attention mechanism is introduced and embedded into the CNN to focus on critical feature channels.Lastly,the weights and biases in the CNN-SE are extracted to initialize the population individuals of the APSO.As the number of iterations increases,the population’s position vector is continuously updated,and the cross-entropy loss function value is minimized to produce the ideal network architecture.We evaluated the models experimentally using binary and multiclassification on the UNSW-NB15 and NSL-KDD datasets,comparing and analyzing the evaluation metrics derived from each model.Compared to the base CNN model,the results demonstrate that APSO-CNNSE enhances the binary classification detection accuracy by 1.84%and 3.53%and the multiclassification detection accuracy by 1.56%and 2.73%on the two datasets,respectively.Additionally,the model outperforms the existing models like DT,KNN,LR,SVM,LSTM,etc.,in terms of accuracy and fitting performance.This means that the model can identify potential attacks or anomalies more precisely,improving the overall security and stability of the IoT environment.
文摘With the development of the economy and the surge in car ownership, the sale of used cars has been welcomed by more and more people, and the information of the vehicle condition is the focus information of them. The frame number is a unique number used in the vehicle, and by identifying it can quickly find out the vehicle models and manufacturers. The traditional character recognition method has the problem of complex feature extraction, and the convolutional neural network has unique advantages in processing two-dimensional images. This paper analyzed the key techniques of convolutional neural networks compared with traditional neural networks, and proposed improved methods for key technologies, thus increasing the recognition of characters and applying them to the recognition of frame number characters.
文摘With the high-speed development of the Internet,a growing number of Internet users like giving their subjective comments in the BBS,blog and shopping website.These comments contains critics’attitudes,emotions,views and other information.Using these information reasonablely can help understand the social public opinion and make a timely response and help dealer to improve quality and service of products and make consumers know merchandise.This paper mainly discusses using convolutional neural network(CNN)for the operation of the text feature extraction.The concrete realization are discussed.Then combining with other text classifier make class operation.The experiment result shows the effectiveness of the method which is proposed in this paper.
基金The National Natural Science Foundation of China(No.6504000089)
文摘A demodulator based on convolutional neural networks（ CNNs） is proposed to demodulate bipolar extended binary phase shifting keying（ EBPSK） signals transmitted at a faster-thanNyquist（ FTN） rate, solving the problem of severe inter symbol interference（ ISI） caused by FTN rate signals. With the characteristics of local connectivity, pooling and weight sharing,a six-layer CNNs structure is used to demodulate and eliminate ISI. The results showthat with the symbol rate of 1. 07 k Bd, the bandwidth of the band-pass filter（ BPF） in a transmitter of 1 k Hz and the changing number of carrier cycles in a symbol K = 5,10,15,28, the overall bit error ratio（ BER） performance of CNNs with single-symbol decision is superior to that with a doublesymbol united-decision. In addition, the BER performance of single-symbol decision is approximately 0. 5 d B better than that of the coherent demodulator while K equals the total number of carrier circles in a symbol, i. e., K = N = 28. With the symbol rate of 1. 07 k Bd, the bandwidth of BPF in a transmitter of 500 Hz and K = 5,10,15,28, the overall BER performance of CNNs with double-symbol united-decision is superior to those with single-symbol decision. Moreover, the double-symbol uniteddecision method is approximately 0. 5 to 1. 5 d B better than that of the coherent demodulator while K = N = 28. The demodulators based on CNNs successfully solve the serious ISI problems generated during the transmission of FTN rate bipolar EBPSK signals, which is beneficial for the improvement of spectrum efficiency.
文摘Nowadays,the amount of wed data is increasing at a rapid speed,which presents a serious challenge to the web monitoring.Text sentiment analysis,an important research topic in the area of natural language processing,is a crucial task in the web monitoring area.The accuracy of traditional text sentiment analysis methods might be degraded in dealing with mass data.Deep learning is a hot research topic of the artificial intelligence in the recent years.By now,several research groups have studied the sentiment analysis of English texts using deep learning methods.In contrary,relatively few works have so far considered the Chinese text sentiment analysis toward this direction.In this paper,a method for analyzing the Chinese text sentiment is proposed based on the convolutional neural network(CNN)in deep learning in order to improve the analysis accuracy.The feature values of the CNN after the training process are nonuniformly distributed.In order to overcome this problem,a method for normalizing the feature values is proposed.Moreover,the dimensions of the text features are optimized through simulations.Finally,a method for updating the learning rate in the training process of the CNN is presented in order to achieve better performances.Experiment results on the typical datasets indicate that the accuracy of the proposed method can be improved compared with that of the traditional supervised machine learning methods,e.g.,the support vector machine method.
