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1
Hsieh, Tien-Heng, and Jean-Fu Kiang. "Comparison of CNN Algorithms on Hyperspectral Image Classification in Agricultural Lands." Sensors 20, no. 6 (March 20, 2020): 1734. http://dx.doi.org/10.3390/s20061734.
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Several versions of convolutional neural network (CNN) were developed to classify hyperspectral images (HSIs) of agricultural lands, including 1D-CNN with pixelwise spectral data, 1D-CNN with selected bands, 1D-CNN with spectral-spatial features and 2D-CNN with principal components. The HSI data of a crop agriculture in Salinas Valley and a mixed vegetation agriculture in Indian Pines were used to compare the performance of these CNN algorithms. The highest overall accuracy on these two cases are 99.8% and 98.1%, respectively, achieved by applying 1D-CNN with augmented input vectors, which contain both spectral and spatial features embedded in the HSI data.
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Ghafoor, Karzan J., Karwan M. Hama Rawf, Ayub O. Abdulrahman, and Sarkhel H. Taher. "Kurdish Dialect Recognition using 1D CNN." ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY 9, no. 2 (October 15, 2021): 10–14. http://dx.doi.org/10.14500/aro.10837.
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Dialect recognition is one of the most attentive topics in the speech analysis area. Machine learning algorithms have been widely used to identify dialects. In this paper, a model that based on three different 1D Convolutional Neural Network (CNN) structures is developed for Kurdish dialect recognition. This model is evaluated, and CNN structures are compared to each other. The result shows that the proposed model has outperformed the state of the art. The model is evaluated on the experimental data that have been collected by the staff of department of computer science at the University of Halabja. Three dialects are involved in the dataset as the Kurdish language consists of three major dialects, namely Northern Kurdish (Badini variant), Central Kurdish (Sorani variant), and Hawrami. The advantage of the CNN model is not required to concern handcraft as the CNN model is featureless. According to the results, the 1 D CNN method can make predictions with an average accuracy of 95.53% on the Kurdish dialect classification. In this study, a new method is proposed to interpret the closeness of the Kurdish dialects by using a confusion matrix and a non-metric multi-dimensional visualization technique. The outcome demonstrates that it is straightforward to cluster given Kurdish dialects and linearly isolated from the neighboring dialects.
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Kim, A. Ran, Ha Seon Kim, Chang Ho Kang, and Sun Young Kim. "The Design of the 1D CNN–GRU Network Based on the RCS for Classification of Multiclass Missiles." Remote Sensing 15, no. 3 (January 18, 2023): 577. http://dx.doi.org/10.3390/rs15030577.
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For real-time target classification, a study was conducted to improve the AI-based target classification performance using RCS measurements that are vulnerable to noise, but can be obtained quickly. To compensate for the shortcomings of the RCS, a 1D CNN–GRU network with strengths in feature extraction and time-series processing was considered. The 1D CNN–GRU was experimentally changed and designed to fit the RCS characteristics. The performance of the proposed 1D CNN–GRU was compared and analyzed using the 1D CNN and 1D CNN–LSTM. The designed 1D CNN–GRU had the best classification performance with a high accuracy of 99.50% in complex situations, such as with different missile shapes with the same trajectory and with the same missile shapes that had the same trajectory. In addition, to confirm the general target classification performance for the RCS, a new class was verified. The 1D CNN–GRU had the highest classification performance at 99.40%. Finally, as a result of comparing three networks by adding noise to compensate for the shortcomings of the RCS, the 1D CNN–GRU, which was optimized for both the data set used in this paper and the newly constructed data set, was the most robust to noise.
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Ma, Xiaotong, Qixia Man, Xinming Yang, Pinliang Dong, Zelong Yang, Jingru Wu, and Chunhui Liu. "Urban Feature Extraction within a Complex Urban Area with an Improved 3D-CNN Using Airborne Hyperspectral Data." Remote Sensing 15, no. 4 (February 10, 2023): 992. http://dx.doi.org/10.3390/rs15040992.
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Airborne hyperspectral data has high spectral-spatial information. However, how to mine and use this information effectively is still a great challenge. Recently, a three-dimensional convolutional neural network (3D-CNN) provides a new effective way of hyperspectral classification. However, its capability of data mining in complex urban areas, especially in cloud shadow areas has not been validated. Therefore, a 3D-1D-CNN model was proposed for feature extraction in complex urban with hyperspectral images affected by cloud shadows. Firstly, spectral composition parameters, vegetation index, and texture characteristics were extracted from hyperspectral data. Secondly, the parameters were fused and segmented into many S × S × B patches which would be input into a 3D-CNN classifier for feature extraction in complex urban areas. Thirdly, Support Vector Machine (SVM), Random Forest (RF),1D-CNN, 3D-CNN, and 3D-2D-CNN classifiers were also carried out for comparison. Finally, a confusion matrix and Kappa coefficient were calculated for accuracy assessment. The overall accuracy of the proposed 3D-1D-CNN is 96.32%, which is 23.96%, 11.02%, 5.22%, and 0.42%, much higher than that of SVM, RF, 1D-CNN, or 3D-CNN, respectively. The results indicated that 3D-1D-CNN could mine spatial-spectral information from hyperspectral data effectively, especially that of grass and highway in cloud shadow areas with missing spectral information. In the future, 3D-1D-CNN could also be used for the extraction of urban green spaces.
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Hou, Wenguang, Shaojie Mei, Qiuling Gui, Yingcheng Zou, Yifan Wang, Xianbo Deng, and Qimin Cheng. "1D CNN-Based Intracranial Aneurysms Detection in 3D TOF-MRA." Complexity 2020 (November 12, 2020): 1–13. http://dx.doi.org/10.1155/2020/7023754.
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How to automatically detect intracranial aneurysms from Three-Dimension Time of Flight Magnetic Resonance Angiography (3D TOF MRA) images is a typical 3D image classification problem. Currently, the commonly used method is the Maximum Intensity Projection- (MIP-) based way. It transfers 3D classification into 2D case by projecting the 3D patch into 2D planes along different directions on the basis of voxel’s intensity. After then, the 2D Convolutional Neural Network (CNN) is established to do classification. It has been shown that the MIP-based method can reduce the demands for the samples and increase the computation efficiency. Meanwhile, the accuracy is comparable with that of 3D image classification. Inspired by the strategy of MIP, we want to further reduce the demands for samples and accelerate the training by transferring the 2D image classification into 1D case, i.e., we want to generate the 1D vectors from the MIP images and then establish a 1D CNN to do intracranial aneurysm detection and classification for 3D TOF MRA image. Specifically, our method first extracts a series of patches as the Region of Interests (ROIs) along the blood vessels from the original 3D TOF MRA 3D image. The corresponding MIP images of each ROI will be obtained through maximum intensity projecting. Then, we generate a series of 1D vectors by accumulating each MIP image along different directions. Meanwhile, a 1D CNN is established to detect aneurysms, in which, the input is the obtained 1D vectors and the output is the binary classification result denoting whether there are intracranial aneurysms in the considered patch. Generally, compared with 2D- and 3D-CNN, the 1D CNN-based way greatly accelerates the training and shows stronger robustness in the case of fewer samples. The efficiency of the proposed method outperforms the 2D CNN about 10 times in CPU training. Yet, their accuracies are close.
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Li, Xingpeng, Hongzhe Jiang, Xuesong Jiang, and Minghong Shi. "Identification of Geographical Origin of Chinese Chestnuts Using Hyperspectral Imaging with 1D-CNN Algorithm." Agriculture 11, no. 12 (December 15, 2021): 1274. http://dx.doi.org/10.3390/agriculture11121274.
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The adulteration in Chinese chestnuts affects the quality, taste, and brand value. The objective of this study was to explore the feasibility of the hyperspectral imaging (HSI) technique to determine the geographical origin of Chinese chestnuts. An HSI system in spectral range of 400–1000 nm was applied to identify a total of 417 Chinese chestnuts from three different geographical origins. Principal component analysis (PCA) was preliminarily used to investigate the differences of average spectra of the samples from different geographical origins. A deep-learning-based model (1D-CNN, one-dimensional convolutional neural network) was developed first, and then the model based on full spectra and optimal wavelengths were established for various machine learning methods, including partial least squares-discriminant analysis (PLS-DA) and particle swarm optimization-support vector machine (PSO-SVM). The optimal results based on full spectra for 1D-CNN, PLS-DA, and PSO-SVM models were 97.12%, 97.12%, and 95.68%, respectively. Competitive adaptive reweighted sampling (CARS) and a successive projections algorithm (SPA) were individually utilized for wavelengths selection, and the results of simplified models generally improved. The contrasting results demonstrated that the prediction accuracies of SPA-PLS-DA and 1D-CNN both reached 97.12%, but 1D-CNN presented a higher Kappa coefficient value than SPA-PLS-DA. Meanwhile, the sensitivities and specificities of SPA-PLS-DA and 1D-CNN models were both above 90% for the samples from each geographical origin. These results indicated that both SPA-PLS-DA and 1D-CNN models combined with HSI have great potential for the geographical origin identification of Chinese chestnuts.
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Yuan, Xinzhe, Dustin Tanksley, Liujun Li, Haibin Zhang, Genda Chen, and Donald Wunsch. "Faster Post-Earthquake Damage Assessment Based on 1D Convolutional Neural Networks." Applied Sciences 11, no. 21 (October 21, 2021): 9844. http://dx.doi.org/10.3390/app11219844.
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Contemporary deep learning approaches for post-earthquake damage assessments based on 2D convolutional neural networks (CNNs) require encoding of ground motion records to transform their inherent 1D time series to 2D images, thus requiring high computing time and resources. This study develops a 1D CNN model to avoid the costly 2D image encoding. The 1D CNN model is compared with a 2D CNN model with wavelet transform encoding and a feedforward neural network (FNN) model to evaluate prediction performance and computational efficiency. A case study of a benchmark reinforced concrete (r/c) building indicated that the 1D CNN model achieved a prediction accuracy of 81.0%, which was very close to the 81.6% prediction accuracy of the 2D CNN model and much higher than the 70.8% prediction accuracy of the FNN model. At the same time, the 1D CNN model reduced computing time by more than 90% and reduced resources used by more than 69%, as compared to the 2D CNN model. Therefore, the developed 1D CNN model is recommended for rapid and accurate resultant damage assessment after earthquakes.
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Byun, Eunseok, and Jongsoo Lee. "Image-based Vibration Signal Measurement and Calibration Using 1D CNN." Transactions of the Korean Society of Mechanical Engineers - A 46, no. 8 (August 31, 2022): 765–72. http://dx.doi.org/10.3795/ksme-a.2022.46.8.765.
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Liu, Yan, Yue Shen, Li Li, and Hai Wang. "FPGA Implementation of a BPSK 1D-CNN Demodulator." Applied Sciences 8, no. 3 (March 15, 2018): 441. http://dx.doi.org/10.3390/app8030441.
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Kim, Jung-Woo, Seung-Ho Park, Sock-Kyu Lee, and Kyoung-Su Park. "Artificial Intelligence Network with 1D-/2D-CNN and LSTM Predicting Flank Wear from Raw Vibration Signals." Transactions of the Korean Society for Noise and Vibration Engineering 32, no. 4 (August 31, 2022): 384–91. http://dx.doi.org/10.5050/ksnve.2022.32.4.384.
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Xu, Yanping, Xia Zhang, Chengdan Lu, Zhenliang Qiu, Chunfang Bi, Yuping Lai, Jian Qiu, and Hua Zhang. "Network Threat Detection Based on Group CNN for Privacy Protection." Wireless Communications and Mobile Computing 2021 (September 3, 2021): 1–18. http://dx.doi.org/10.1155/2021/3697536.
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The Internet of Things (IoT) contains a large amount of data, which attracts various types of network attacks that lead to privacy leaks. With the upgrading of network attacks and the increase in network security data, traditional machine learning methods are no longer suitable for network threat detection. At the same time, data analysis techniques and deep learning algorithms have developed rapidly and have been successfully applied to a variety of tasks for privacy protection. Convolutional neural networks (CNNs) are typical deep learning models that can learn and reconstruct features accurately and efficiently. Therefore, in this paper, we propose a group CNN models that is based on feature correlations to learn features and reconstruct security data. First, feature correlation coefficients are computed to measure the relationships among the features. Then, we sort the correlation coefficients in descending order and group the data by columns. Second, a 1D group CNN model with multiple 1D convolution kernels and 1D pooling filters is built to address the grouped data for feature learning and reconstruction. Third, the reconstructed features are input to shadow machine learning models for network threat prediction. The experimental results show that features reconstructed by the group CNN can reduce the dimensions and achieve the best performance compared to the other present dimension reduction algorithms. At the same time, the group CNN can decrease the floating point of operations (FLOP), parameters, and running time compared to the basic 1D CNN.
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Li, Dengshan, and Lina Li. "Detection of Water pH Using Visible Near-Infrared Spectroscopy and One-Dimensional Convolutional Neural Network." Sensors 22, no. 15 (August 3, 2022): 5809. http://dx.doi.org/10.3390/s22155809.
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pH is an important parameter for water quality detection. This study proposed a novel calibration regression strategy based on a one-dimensional convolutional neural network (1D-CNN) for water pH detection using visible near-infrared (Vis-NIR) spectroscopy. Two groups of Vis-NIR spectral analysis experiments of water pH detection were employed to evaluate the performance of 1D-CNN. Two conventional multivariate regression calibration methods, including partial least squares (PLS) and least squares support vector machine (LS-SVM), were introduced for comparative analysis with 1D-CNN. The successive projections algorithm (SPA) was adopted to select the feature variables. In addition, the learning mechanism of 1D-CNN was interpreted through visual feature maps by convolutional layers. The results showed that the 1D-CNN models obtained the highest prediction accuracy based on full spectra for the two experiments. For the spectrophotometer experiment, the root mean square error of prediction (RMSEP) was 0.7925, and the determination coefficient of prediction (Rp2) was 0.8515. For the grating spectrograph experiment, the RMSEP was 0.5128 and the Rp2 was 0.9273. The convolutional layers could automatically preprocess the spectra and effectively extract the spectra features. Compared with the traditional regression methods, 1D-CNN does not need complex spectra pretreatment and variable selection. Therefore, 1D-CNN is a promising regression approach, with higher prediction accuracy and better modeling convenience for rapid water pH detection using Vis-NIR spectroscopy.
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Oyewola, David Opeoluwa, Emmanuel Gbenga Dada, Temidayo Oluwatosin Omotehinwa, Onyeka Emebo, and Olugbenga Oluseun Oluwagbemi. "Application of Deep Learning Techniques and Bayesian Optimization with Tree Parzen Estimator in the Classification of Supply Chain Pricing Datasets of Health Medications." Applied Sciences 12, no. 19 (October 10, 2022): 10166. http://dx.doi.org/10.3390/app121910166.
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From the development and sale of a product through its delivery to the end customer, the supply chain encompasses a network of suppliers, transporters, warehouses, distribution centers, shipping lines, and logistics service providers all working together. Lead times, bottlenecks, cash flow, data management, risk exposure, traceability, conformity, quality assurance, flaws, and language barriers are some of the difficulties that supply chain management faces. In this paper, deep learning techniques such as Long Short-Term Memory (LSTM) and One Dimensional Convolutional Neural Network (1D-CNN) were adopted and applied to classify supply chain pricing datasets of health medications. Then, Bayesian optimization using the tree parzen estimator and All K Nearest Neighbor (AllkNN) was used to establish the suitable model hyper-parameters of both LSTM and 1D-CNN to enhance the classification model. Repeated five-fold cross-validation is applied to the developed models to predict the accuracy of the models. The study showed that the combination of 1D-CNN, AllkNN, and Bayesian optimization (1D-CNN+AllKNN+BO) outperforms other approaches employed in this study. The accuracy of the combination of 1D-CNN, AllkNN, and Bayesian optimization (1D-CNN+AllKNN+BO) from one-fold to 10-fold, produced the highest range between 61.2836% and 63.3267%, among other models.
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Guessoum, Sonia, Santiago Belda, Jose M. Ferrandiz, Sadegh Modiri, Shrishail Raut, Sujata Dhar, Robert Heinkelmann, and Harald Schuh. "The Short-Term Prediction of Length of Day Using 1D Convolutional Neural Networks (1D CNN)." Sensors 22, no. 23 (December 6, 2022): 9517. http://dx.doi.org/10.3390/s22239517.
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Accurate Earth orientation parameter (EOP) predictions are needed for many applications, e.g., for the tracking and navigation of interplanetary spacecraft missions. One of the most difficult parameters to forecast is the length of day (LOD), which represents the variation in the Earth’s rotation rate since it is primarily affected by the torques associated with changes in atmospheric circulation. In this study, a new-generation time-series prediction algorithm is developed. The one-dimensional convolutional neural network (1D CNN), which is one of the deep learning methods, is introduced to model and predict the LOD using the IERS EOP 14 C04 and axial Z component of the atmospheric angular momentum (AAM), which was taken from the German Research Centre for Geosciences (GFZ) since it is strongly correlated with the LOD changes. The prediction procedure operates as follows: first, we detrend the LOD and Z-component series using the LS method, then, we obtain the residual series of each one to be used in the 1D CNN prediction algorithm. Finally, we analyze the results before and after introducing the AAM function. The results prove the potential of the proposed method as an optimal algorithm to successfully reconstruct and predict the LOD for up to 7 days.
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Perattur, Nagabushanam, S. Thomas George, D. Raveena Judie Dolly, and Radha Subramanyam. "Convolution Neural Network Architectures for Motor Imagery EEG Signal Classification." International Journal of Artificial Intelligence and Machine Learning 11, no. 1 (January 2021): 15–22. http://dx.doi.org/10.4018/ijaiml.2021010102.
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This paper has made a survey on motor imagery EEG signals and different classifiers to analyze them. Resolution for medical images like CT, MRI can be improved using deep sense CNN and improved resolution technology. Drowsiness of a student can be analyzed using deep CNN and it helps in teaching, assessment of the student. The authors have proposed 1D-CNN with 2 layers and 3 layers architecture to classify EEG signal for eyes open and eyes closed conditions. Various activation functions and combinations are tried for 2-layer 1D-CNN. Similarly, various loss models are applied in compile model to check the CNN performance. Simulation is carried out using Python 2.7 and 1D-CNN with 3 layers show better performance as it increases number of training parameters by increasing number of layers in the architecture. Accuracy and kappa coefficient increase whereas hamming loss and logloss decreases by increasing number of layers in CNN architecture.
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Rala Cordeiro, João, António Raimundo, Octavian Postolache, and Pedro Sebastião. "Neural Architecture Search for 1D CNNs—Different Approaches Tests and Measurements." Sensors 21, no. 23 (November 30, 2021): 7990. http://dx.doi.org/10.3390/s21237990.
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In the field of sensors, in areas such as industrial, clinical, or environment, it is common to find one dimensional (1D) formatted data (e.g., electrocardiogram, temperature, power consumption). A very promising technique for modelling this information is the use of One Dimensional Convolutional Neural Networks (1D CNN), which introduces a new challenge, namely how to define the best architecture for a 1D CNN. This manuscript addresses the concept of One Dimensional Neural Architecture Search (1D NAS), an approach that automates the search for the best combination of Neuronal Networks hyperparameters (model architecture), including both structural and training hyperparameters, for optimising 1D CNNs. This work includes the implementation of search processes for 1D CNN architectures based on five strategies: greedy, random, Bayesian, hyperband, and genetic approaches to perform, collect, and analyse the results obtained by each strategy scenario. For the analysis, we conducted 125 experiments, followed by a thorough evaluation from multiple perspectives, including the best-performing model in terms of accuracy, consistency, variability, total running time, and computational resource consumption. Finally, by presenting the optimised 1D CNN architecture, the results for the manuscript’s research question (a real-life clinical case) were provided.
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Fukuoka, Rui, Hiroshi Suzuki, Takahiro Kitajima, Akinobu Kuwahara, and Takashi Yasuno. "Wind Speed Prediction Model Using LSTM and 1D-CNN." Journal of Signal Processing 22, no. 4 (July 25, 2018): 207–10. http://dx.doi.org/10.2299/jsp.22.207.
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Li, Shuyan, Zhixiang Chen, Xiu Li, Jiwen Lu, and Jie Zhou. "Unsupervised Variational Video Hashing With 1D-CNN-LSTM Networks." IEEE Transactions on Multimedia 22, no. 6 (June 2020): 1542–54. http://dx.doi.org/10.1109/tmm.2019.2946096.
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Li, Xingwang, Xiaofei Fan, Lili Zhao, Sheng Huang, Yi He, and Xuesong Suo. "Discrimination of Pepper Seed Varieties by Multispectral Imaging Combined with Machine Learning." Applied Engineering in Agriculture 36, no. 5 (2020): 743–49. http://dx.doi.org/10.13031/aea.13794.
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HighlightsThis study revealed the feasibility of to classify pepper seed varieties using multispectral imaging combined with one-dimensional convolutional neural network (1D-CNN).Convolutional neural networks were adopted to develop models for prediction of seed varieties, and the performance was compared with KNN and SVM.In this experiment, the classification effect of the SVM classification model is the best, but the 1D-CNN classification model is relatively easy to implement.Abstract. When non-seed materials are mixed in seeds or seed varieties of low value are mixed in high value varieties, it will cause losses to growers or businesses. Thus, the successful discrimination of seed varieties is critical for improvement of seed ralue. In recent years, convolutional neural networks (CNNs) have been used in classification of seed varieties. The feasibility of using multispectral imaging combined with one-dimensional convolutional neural network (1D-CNN) to classify pepper seed varieties was studied. The total number of three varieties of samples was 1472, and the average spectral curve between 365nm and 970nm of the three varieties was studied. The data were analyzed using full bands of the spectrum or the feature bands selected by successive projection algorithm (SPA). SPA extracted 9 feature bands from 19 bands (430, 450, 470, 490, 515, 570, 660, 780, and 880 nm). The classification accuracy of the three classification models developed with full band using K nearest neighbors (KNN), support vector machine (SVM), and 1D-CNN were 85.81%, 97.70%, and 90.50%, respectively. With full bands, SVM and 1D-CNN performed significantly better than KNN, and SVM performed slightly better than 1D-CNN. With feature bands, the testing accuracies of SVM and 1D-CNN were 97.30% and 92.6%, respectively. Although the classification accuracy of 1D-CNN was not the highest, the ease of operation made it the most feasible method for pepper seed variety prediction. Keywords: Multispectral imaging, One-dimensional convolutional neural network, Pepper seed, Variety classification.
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Cheng, Hu, Sophia Vinci-Booher, Jian Wang, Bradley Caron, Qiuting Wen, Sharlene Newman, and Franco Pestilli. "Denoising diffusion weighted imaging data using convolutional neural networks." PLOS ONE 17, no. 9 (September 15, 2022): e0274396. http://dx.doi.org/10.1371/journal.pone.0274396.
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Diffusion weighted imaging (DWI) with multiple, high b-values is critical for extracting tissue microstructure measurements; however, high b-value DWI images contain high noise levels that can overwhelm the signal of interest and bias microstructural measurements. Here, we propose a simple denoising method that can be applied to any dataset, provided a low-noise, single-subject dataset is acquired using the same DWI sequence. The denoising method uses a one-dimensional convolutional neural network (1D-CNN) and deep learning to learn from a low-noise dataset, voxel-by-voxel. The trained model can then be applied to high-noise datasets from other subjects. We validated the 1D-CNN denoising method by first demonstrating that 1D-CNN denoising resulted in DWI images that were more similar to the noise-free ground truth than comparable denoising methods, e.g., MP-PCA, using simulated DWI data. Using the same DWI acquisition but reconstructed with two common reconstruction methods, i.e. SENSE1 and sum-of-square, to generate a pair of low-noise and high-noise datasets, we then demonstrated that 1D-CNN denoising of high-noise DWI data collected from human subjects showed promising results in three domains: DWI images, diffusion metrics, and tractography. In particular, the denoised images were very similar to a low-noise reference image of that subject, more than the similarity between repeated low-noise images (i.e. computational reproducibility). Finally, we demonstrated the use of the 1D-CNN method in two practical examples to reduce noise from parallel imaging and simultaneous multi-slice acquisition. We conclude that the 1D-CNN denoising method is a simple, effective denoising method for DWI images that overcomes some of the limitations of current state-of-the-art denoising methods, such as the need for a large number of training subjects and the need to account for the rectified noise floor.
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Ullah, Amin, Sadaqat ur Rehman, Shanshan Tu, Raja Majid Mehmood, Fawad, and Muhammad Ehatisham-ul-haq. "A Hybrid Deep CNN Model for Abnormal Arrhythmia Detection Based on Cardiac ECG Signal." Sensors 21, no. 3 (February 1, 2021): 951. http://dx.doi.org/10.3390/s21030951.
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Electrocardiogram (ECG) signals play a vital role in diagnosing and monitoring patients suffering from various cardiovascular diseases (CVDs). This research aims to develop a robust algorithm that can accurately classify the electrocardiogram signal even in the presence of environmental noise. A one-dimensional convolutional neural network (CNN) with two convolutional layers, two down-sampling layers, and a fully connected layer is proposed in this work. The same 1D data was transformed into two-dimensional (2D) images to improve the model’s classification accuracy. Then, we applied the 2D CNN model consisting of input and output layers, three 2D-convolutional layers, three down-sampling layers, and a fully connected layer. The classification accuracy of 97.38% and 99.02% is achieved with the proposed 1D and 2D model when tested on the publicly available Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. Both proposed 1D and 2D CNN models outperformed the corresponding state-of-the-art classification algorithms for the same data, which validates the proposed models’ effectiveness.
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Mustaqeem and Soonil Kwon. "1D-CNN: Speech Emotion Recognition System Using a Stacked Network with Dilated CNN Features." Computers, Materials & Continua 67, no. 3 (2021): 4039–59. http://dx.doi.org/10.32604/cmc.2021.015070.
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Kim, Seong-Hoon, Zong Woo Geem, and Gi-Tae Han. "Hyperparameter Optimization Method Based on Harmony Search Algorithm to Improve Performance of 1D CNN Human Respiration Pattern Recognition System." Sensors 20, no. 13 (July 1, 2020): 3697. http://dx.doi.org/10.3390/s20133697.
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In this study, we propose a method to find an optimal combination of hyperparameters to improve the accuracy of respiration pattern recognition in a 1D (Dimensional) convolutional neural network (CNN). The proposed method is designed to integrate with a 1D CNN using the harmony search algorithm. In an experiment, we used the depth of the convolutional layer of the 1D CNN, the number and size of kernels in each layer, and the number of neurons in the dense layer as hyperparameters for optimization. The experimental results demonstrate that the proposed method provided a recognition rate for five respiration patterns of approximately 96.7% on average, which is an approximately 2.8% improvement over an existing method. In addition, the number of iterations required to derive the optimal combination of hyperparameters was 2,000,000 in the previous study. In contrast, the proposed method required only 3652 iterations.
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Zhang, Dengyong, Haoting Zhou, Feng Li, Lebing Zhang, and Jianxin Wang. "A Reparameterization Multifeature Fusion CNN for Arrhythmia Heartbeats Classification." Computational and Mathematical Methods in Medicine 2022 (November 23, 2022): 1–9. http://dx.doi.org/10.1155/2022/7401175.
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Aiming at arrhythmia heartbeats classification, a novel multifeature fusion deep learning-based method is proposed. The stationary wavelet transforms (SWT) and RR interval features are firstly extracted. Based on the traditional one-dimensional convolutional neural network (1D-CNN), a parallel multibranch convolutional network is designed for training. The subband of SWT is input into the multiscale 1D-CNN separately. The output fused with RR interval features are fed to the fully connected layer for classification. To achieve the lightweight network while maintaining the powerful inference capability of the multibranch structure, the redundant branches of the network are removed by reparameterization. Experimental results and analysis show that it outperforms existing methods by many in arrhythmic heartbeat classification.
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Yu, Fajun, Liang Liao, Kun Zhang, Hechen Xing, Qifeng Zhao, Liming Zhang, and Zheng Luo. "A Novel 1D-CNN-Based Diagnosis Method for a Rolling Bearing with Dual-Sensor Vibration Data Fusion." Mathematical Problems in Engineering 2022 (July 7, 2022): 1–13. http://dx.doi.org/10.1155/2022/8986900.
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Due to variation of working conditions and influence of noise in vibration data, rolling bearing intelligent diagnosis based on deep learning faces challenges in efficient utilization of monitoring data and scientific extraction of fault features. This study proposes a one-dimensional convolution neural network (1D-CNN)-based intelligent diagnosis method for a rolling bearing, which fuses the horizontal and the vertical vibration signals, makes full use of spectral order features by full-spectrum analysis, and achieves accurate classification of fault pattern by 1D-CNN model. The experimental datasets of constant and variable working conditions of rolling bearing are constructed. The test results of the proposed method show that spectral order features are extracted effectively by full-spectrum analysis and high diagnostic accuracy is obtained by the constructed 1D-CNN model on both datasets. The comparison with the other four similar methods indicates that the diagnostic accuracy of the proposed method outperforms the comparative methods significantly in the case of variable operating conditions.
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Tu, Yuan-Kai, Chin-En Kuo, Shih-Lun Fang, Han-Wei Chen, Ming-Kun Chi, Min-Hwi Yao, and Bo-Jein Kuo. "A 1D-SP-Net to Determine Early Drought Stress Status of Tomato (Solanum lycopersicum) with Imbalanced Vis/NIR Spectroscopy Data." Agriculture 12, no. 2 (February 11, 2022): 259. http://dx.doi.org/10.3390/agriculture12020259.
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Detection of the early stages of stress is crucial in stabilizing crop yields and agricultural production. The aim of this study was to construct a nondestructive and robust method to predict the early physiological drought status of the tomato (Solanum lycopersicum); for this purpose, a convolutional neural network (CNN)-based model with a one-dimensional (1D) kernel for fitting the visible and near infrared (Vis/NIR) spectral data was proposed. To prevent degradation and enhance the feature comprehension of the deep neural network architecture, residual and global context modules were embedded in the proposed 1D-CNN model, yielding the 1D spectrogram power net (1D-SP-Net). The 1D-SP-Net outperformed the 1D-CNN, partial least squares discriminant analysis (PLSDA), and random forest (RF) models in model testing, demonstrating an accuracy of 96.3%, precision of 98.0%, Matthew’s correlation coefficient of 0.92, and an F1 score of 0.95. Furthermore, when employing various synthesized imbalanced data sets, the proposed 1D-SP-Net remained robust and consistent, outperforming the other models in terms of the prediction capabilities. These results indicate that the 1D-SP-Net is a promising model resistant to the effects of imbalanced data sets and able to determine the early drought stress status of tomato seedlings in a non-invasive manner.
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Hsieh, Chaur-Heh, Yan-Shuo Li, Bor-Jiunn Hwang, and Ching-Hua Hsiao. "Detection of Atrial Fibrillation Using 1D Convolutional Neural Network." Sensors 20, no. 7 (April 10, 2020): 2136. http://dx.doi.org/10.3390/s20072136.
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The automatic detection of atrial fibrillation (AF) is crucial for its association with the risk of embolic stroke. Most of the existing AF detection methods usually convert 1D time-series electrocardiogram (ECG) signal into 2D spectrogram to train a complex AF detection system, which results in heavy training computation and high implementation cost. This paper proposes an AF detection method based on an end-to-end 1D convolutional neural network (CNN) architecture to raise the detection accuracy and reduce network complexity. By investigating the impact of major components of a convolutional block on detection accuracy and using grid search to obtain optimal hyperparameters of the CNN, we develop a simple, yet effective 1D CNN. Since the dataset provided by PhysioNet Challenge 2017 contains ECG recordings with different lengths, we also propose a length normalization algorithm to generate equal-length records to meet the requirement of CNN. Experimental results and analysis indicate that our method of 1D CNN achieves an average F1 score of 78.2%, which has better detection accuracy with lower network complexity, as compared with the existing deep learning-based methods.
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Li, Menghu, Tianhong Pan, Yang Bai, and Qi Chen. "Development of a calibration model for near infrared spectroscopy using a convolutional neural network." Journal of Near Infrared Spectroscopy 30, no. 2 (February 25, 2022): 89–96. http://dx.doi.org/10.1177/09670335211057234.
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Development of qualitative or quantitative models is essential to exploit the full potential of near infrared (NIR) spectroscopy. In tandem with one-dimensional convolutional neural network (1D-CNN), a data-driven model is developed using NIR spectroscopy to estimate organic contents. First, the 1D-CNN model is designed to capture the features of the NIR spectra by means of several convolutional and pooling operations. Then, the suitable hyper-parameters of 1D-CNN are obtained by using the grid search algorithm to achieve the optimal performance. Furthermore, the dropout operation is added into the 1D-CNN to suppress the overfitting problem by means of removing some neurons, and the probability distribution of throwing follows the Bernoulli distribution. The developed framework is validated by the application in the sugar content estimation of Huangshan Maofeng tea. The experimental results demonstrate that the key features of the NIR spectra are successfully extracted by the proposed strategy; thereby, a new effective scheme for analyzing NIR spectra is provided for food analysis.
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Kawamura, Kensuke, Tomohiro Nishigaki, Andry Andriamananjara, Hobimiarantsoa Rakotonindrina, Yasuhiro Tsujimoto, Naoki Moritsuka, Michel Rabenarivo, and Tantely Razafimbelo. "Using a One-Dimensional Convolutional Neural Network on Visible and Near-Infrared Spectroscopy to Improve Soil Phosphorus Prediction in Madagascar." Remote Sensing 13, no. 8 (April 15, 2021): 1519. http://dx.doi.org/10.3390/rs13081519.
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As a proximal soil sensing technique, laboratory visible and near-infrared (Vis-NIR) spectroscopy is a promising tool for the quantitative estimation of soil properties. However, there remain challenges for predicting soil phosphorus (P) content and availability, which requires a reliable model applicable for different land-use systems to upscale. Recently, a one-dimensional convolutional neural network (1D-CNN) corresponding to the spectral information of soil was developed to considerably improve the accuracy of soil property predictions. The present study investigated the predictive ability of a 1D-CNN model to estimate soil available P (oxalate-extractable P; Pox) content in soils by comparing it with partial least squares (PLS) and random forest (RF) regressions using soil samples (n = 318) collected from natural (forest and non-forest) and cultivated (upland and flooded rice fields) systems in Madagascar. Overall, the 1D-CNN model showed the best predictive accuracy (R2 = 0.878) with a highly accurate prediction ability (ratio of performance to the interquartile range = 2.492). Compared to the PLS model, the RF and 1D-CNN models indicated 4.37% and 23.77% relative improvement in root mean squared error values, respectively. Based on a sensitivity analysis, the important wavebands for predicting soil Pox were associated with iron (Fe) oxide, organic matter (OM), and water absorption, which were previously known wavelength regions for estimating P in soil. These results suggest that 1D-CNN corresponding spectral signatures can be expected to significantly improve the predictive ability for estimating soil available P (Pox) from Vis-NIR spectral data. Rapid and accurate estimation of available P content in soils using our results can be expected to contribute to effective fertilizer management in agriculture and the sustainable management of ecosystems. However, the 1D-CNN model will require a large dataset to extend its applicability to other regions of Madagascar. Thus, further updates should be tested in future studies using larger datasets from a wide range of ecosystems in the tropics.
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Ren, Junxiao, Weidong Jin, Liang Li, Yunpu Wu, and Zhang Sun. "Performance Degradation Estimation of High-Speed Train Bogie Based on 1D-ConvLSTM Time-Distributed Convolutional Neural Network." Computational Intelligence and Neuroscience 2022 (February 26, 2022): 1–16. http://dx.doi.org/10.1155/2022/5030175.
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High-speed train bogies are essential for the safety and comfort of train operation. The performance of the bogie usually degrades before it fails, so it is necessary to detect the performance degradation of a high-speed train bogie in advance. In this paper, with two key dampers on the bogie taken as experimental objects (lateral damper and yaw damper), a novel 1D-ConvLSTM time-distributed convolutional neural network (CLTD-CNN) is proposed to estimate the performance degradation of a high-speed train bogie. The proposed CLTD-CNN is an encoder-decoder structure. Specifically, the encoder part of the proposed structure consists of a time-distributed 1D-CNN module and a 1D-ConvLSTM. The decoder part consists of a 1D-ConvLSTM and a simple time-CNN with residual connections. In addition, an auxiliary training part is introduced into the structure to support CLTD-CNN in learning the performance degradation trend characteristic, and a special input format is designed for this structure. The whole structure is end-to-end and does not require expert knowledge or engineering experience. The effectiveness of the proposed CLTD-CNN is tested by the high-speed train CRH380A under different performance states. The experimental results demonstrate the superiority of CLTD-CNN. Compared to other methods, the estimation error of CLTD-CNN is the smallest.
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Shen, Xingfa, Zhenxian Ni, Lili Liu, Jian Yang, and Kabir Ahmed. "WiPass: 1D-CNN-based smartphone keystroke recognition Using WiFi signals." Pervasive and Mobile Computing 73 (June 2021): 101393. http://dx.doi.org/10.1016/j.pmcj.2021.101393.
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Shao, Xiaorui, and Chang-Soo Kim. "Unsupervised Domain Adaptive 1D-CNN for Fault Diagnosis of Bearing." Sensors 22, no. 11 (May 30, 2022): 4156. http://dx.doi.org/10.3390/s22114156.
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Fault diagnosis (FD) plays a vital role in building a smart factory regarding system reliability improvement and cost reduction. Recent deep learning-based methods have been applied for FD and have obtained excellent performance. However, most of them require sufficient historical labeled data to train the model which is difficult and sometimes not available. Moreover, the big size model increases the difficulties for real-time FD. Therefore, this article proposed a domain adaptive and lightweight framework for FD based on a one-dimension convolutional neural network (1D-CNN). Particularly, 1D-CNN is designed with a structure of autoencoder to extract the rich, robust hidden features with less noise from source and target data. The extracted features are processed by correlation alignment (CORAL) to minimize domain shifts. Thus, the proposed method could learn robust and domain-invariance features from raw signals without any historical labeled target domain data for FD. We designed, trained, and tested the proposed method on CRWU bearing data sets. The sufficient comparative analysis confirmed its effectiveness for FD.
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Lawal, Abdulmajid, Shafiqur Rehman, Luai M. Alhems, and Md Mahbub Alam. "Wind Speed Prediction Using Hybrid 1D CNN and BLSTM Network." IEEE Access 9 (2021): 156672–79. http://dx.doi.org/10.1109/access.2021.3129883.
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Lin, Lin, Xuri Chen, Ying Shen, and Lin Zhang. "Towards Automatic Depression Detection: A BiLSTM/1D CNN-Based Model." Applied Sciences 10, no. 23 (December 4, 2020): 8701. http://dx.doi.org/10.3390/app10238701.
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Depression is a global mental health problem, the worst cases of which can lead to self-injury or suicide. An automatic depression detection system is of great help in facilitating clinical diagnosis and early intervention of depression. In this work, we propose a new automatic depression detection method utilizing speech signals and linguistic content from patient interviews. Specifically, the proposed method consists of three components, which include a Bidirectional Long Short-Term Memory (BiLSTM) network with an attention layer to deal with linguistic content, a One-Dimensional Convolutional Neural Network (1D CNN) to deal with speech signals, and a fully connected network integrating the outputs of the previous two models to assess the depressive state. Evaluated on two publicly available datasets, our method achieves state-of-the-art performance compared with the existing methods. In addition, our method utilizes audio and text features simultaneously. Therefore, it can get rid of the misleading information provided by the patients. As a conclusion, our method can automatically evaluate the depression state and does not require an expert to conduct the psychological evaluation on site. Our method greatly improves the detection accuracy, as well as the efficiency.
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Guan, Lei, Fangming Hu, Fadi Al-Turjman, Muhammad Bilal Khan, and Xiaodong Yang. "A Non-Contact Paraparesis Detection Technique Based on 1D-CNN." IEEE Access 7 (2019): 182280–88. http://dx.doi.org/10.1109/access.2019.2959023.
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Mei, Zhanyong, Kamen Ivanov, Guoru Zhao, Yuanyuan Wu, Mingzhe Liu, and Lei Wang. "Foot type classification using sensor-enabled footwear and 1D-CNN." Measurement 165 (December 2020): 108184. http://dx.doi.org/10.1016/j.measurement.2020.108184.
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Yu, Yongchao, Qi Liu, Boon Siew Han, and Wei Zhou. "Application of Convolutional Neural Network to Defect Diagnosis of Drill Bits." Applied Sciences 12, no. 21 (October 25, 2022): 10799. http://dx.doi.org/10.3390/app122110799.
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Drilling, one of the most used machining processes, has wide application in different industrial fields. Monitoring the system health and operation status of the drilling process is essential for maintaining production efficiency. In this study, a convolutional neural network (CNN), a deep-learning method, is applied to the defect diagnosis of drill bits. Four drill bits with different health conditions were used to drill holes in an aluminum block, and a vibration sensor collected the signals. Vibration spectrograms generated using short-time Fourier transform were applied to a 2D CNN algorithm, and they were then reconstructed into a 1D data set and applied to a 1D CNN algorithm. The input data size was reduced significantly compared to the raw vibration data after the data-reconstruction process. As a result, the 2D CNN process shows a diagnostic accuracy of 97.33%. On the other hand, the 1D CNN provides a diagnostic accuracy of 96.6%, but it only requires 2/3 of the computational time required by the 2D CNN.
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Liu, Mingping, Xihao Sun, Qingnian Wang, and Suhui Deng. "Short-Term Load Forecasting Using EMD with Feature Selection and TCN-Based Deep Learning Model." Energies 15, no. 19 (September 29, 2022): 7170. http://dx.doi.org/10.3390/en15197170.
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Short-term load forecasting (STLF) has a significant role in reliable operation and efficient scheduling of power systems. However, it is still a major challenge to accurately predict power load due to social and natural factors, such as temperature, humidity, holidays and weekends, etc. Therefore, it is very important for the efficient feature selection and extraction of input data to improve the accuracy of STLF. In this paper, a novel hybrid model based on empirical mode decomposition (EMD), a one-dimensional convolutional neural network (1D-CNN), a temporal convolutional network (TCN), a self-attention mechanism (SAM), and a long short-term memory network (LSTM) is proposed to fully decompose the input data and mine the in-depth features to improve the accuracy of load forecasting. Firstly, the original load sequence was decomposed into a number of sub-series by the EMD, and the Pearson correlation coefficient method (PCC) was applied for analyzing the correlation between the sub-series with the original load data. Secondly, to achieve the relationships between load series and external factors during an hour scale and the correlations among these data points, a strategy based on the 1D-CNN and TCN is proposed to comprehensively refine the feature extraction. The SAM was introduced to further enhance the key feature information. Finally, the feature matrix was fed into the long short-term memory (LSTM) for STLF. According to experimental results employing the North American New England Control Area (ISO-NE-CA) dataset, the proposed model is more accurate than 1D-CNN, LSTM, TCN, 1D-CNN–LSTM, and TCN–LSTM models. The proposed model outperforms the 1D-CNN, LSTM, TCN, 1D-CNN–LSTM, and TCN–LSTM by 21.88%, 51.62%, 36.44%, 42.75%, 16.67% and 40.48%, respectively, in terms of the mean absolute percentage error.
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Li, Heping, Jing Lu, Guixiang Tian, Huijin Yang, Jianhui Zhao, and Ning Li. "Crop Classification Based on GDSSM-CNN Using Multi-Temporal RADARSAT-2 SAR with Limited Labeled Data." Remote Sensing 14, no. 16 (August 11, 2022): 3889. http://dx.doi.org/10.3390/rs14163889.
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Crop classification is an important part of crop management and yield estimation. In recent years, neural networks have made great progress in synthetic aperture radar (SAR) crop classification. However, the insufficient number of labeled samples limits the classification performance of neural networks. In order to solve this problem, a new crop classification method combining geodesic distance spectral similarity measurement and a one-dimensional convolutional neural network (GDSSM-CNN) is proposed in this study. The method consisted of: (1) the geodesic distance spectral similarity method (GDSSM) for obtaining similarity and (2) the one-dimensional convolutional neural network model for crop classification. Thereinto, a large number of training data are extracted by GDSSM and the generalized volume scattering model which is based on radar vegetation index (GRVI), and then classified by 1D-CNN. In order to prove the effectiveness of the GDSSM-CNN method, the GDSSM method and 1D-CNN method are compared in the case of a limited sample. In terms of evaluation and verification of methods, the GDSSM-CNN method has the highest accuracy, with an accuracy rate of 91.2%, which is 19.94% and 23.91% higher than the GDSSM method and the 1D-CNN method, respectively. In general, the GDSSM-CNN method uses a small number of ground measurement samples, and it uses the rich polarity information in multi-temporal fully polarized SAR data to obtain a large number of training samples, which can quickly improve the accuracy of classification in a short time, which has more new inspiration for crop classification.
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Chen, Yeong-Chin, M. Syamsudin, and S. S. Berutu. "Regulated 2D Grayscale Image for Finding Power Quality Abnormalities in Actual Data." Journal of Physics: Conference Series 2347, no. 1 (September 1, 2022): 012018. http://dx.doi.org/10.1088/1742-6596/2347/1/012018.
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Abstract It is possible to preserve power quality by classifying and identifying abnormalities. Prior studies focused on enhancing the PQD classification performance in one-dimensional (1D) CNNs. Recently, various image conversion methods have been established to facilitate CNN for PQD classification. PQD is a 1D signal that needs to be converted to a 2D image through data pre-processing since 2D images may include more PQD information than 1D signals. However, the PQD data used for the power quality classifier is synthetic PQD produced using mathematical models with parameter modifications in accordance with IEEE Std. 1159, which places limitations on prior research. This study uses data from the Amrita Honeywell Hackathon 2021 to examine how the response-based 2D deep CNN power quality classifier responds to actual field power quality disruptions. The results of the study show that a 2D deep CNN with regulated 2D grayscale pictures based on a process-regulated 2D image matrix can classify real data power quality disturbances with accuracy, precision, recall, and F1-score of 98.80%, 98.99%, and 98.60%, respectively. Additionally, 2D images can potentially contain more PQD data than 1D signals, enhancing identification performance on actual data.
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Hidayat, Erwin Yudi, and Devioletta Handayani. "Penerapan 1D-CNN untuk Analisis Sentimen Ulasan Produk Kosmetik Berdasar Female Daily Review." Jurnal Nasional Teknologi dan Sistem Informasi 8, no. 3 (January 14, 2023): 153–63. http://dx.doi.org/10.25077/teknosi.v8i3.2022.153-163.
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Pada tahun 2020 tercatat sekitar 797 industri kosmetik berskala besar maupun kecil yang terdapat di Indonesia. Berdasarkan tahun sebelumnya, angka ini naik 4.87%. Kondisi ini menyebabkan munculnya persaingan perusahaan kosmetik, salah satunya adalah Emina. Berbagai media digunakan sebagai sarana untuk menyampaikan sentimen atau opini masyarakat. Pihak perusahaan dapat memanfaatkan sentimen untuk mengetahui umpan balik masyarakat terhadap brand mereka. Website Female Daily Review menjadi salah satu platform yang digunakan untuk menampung segala bentuk opini mengenai produk kecantikan. Proses pengambilan data dari website pada penelitian ini menggunakan web scraping. Dari 11119 data ulasan yang didapatkan diperlukan analisis opini, emosi, dan sentimennya dengan memanfaatkan text mining untuk identifikasi serta mengekstrak suatu topik. Analisis sentimen dapat membantu mengetahui tingkat kepuasan pengguna terhadap suatu brand kosmetik. Algoritma yang digunakan adalah 1D-Convolutional Neural Network (1D-CNN). Sebelum dilakukan klasifikasi data, perlu diterapkan text preprocessing agar dataset mentah menjadi lebih terstruktur. Hasil dari klasifikasi sentimen dibagi ke dalam 3 kategori yaitu positif, negatif, dan netral. Berdasarkan eksperimen dalam membangun model analisis sentimen menggunakan 1D-CNN sebanyak 30 percobaan, didapatkan model terbaik dalam menganalisis sentimen dengan akurasi sebesar 80.22%.
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Sarra, Raniya R., Ahmed M. Dinar, Mazin Abed Mohammed, Mohd Khanapi Abd Ghani, and Marwan Ali Albahar. "A Robust Framework for Data Generative and Heart Disease Prediction Based on Efficient Deep Learning Models." Diagnostics 12, no. 12 (November 22, 2022): 2899. http://dx.doi.org/10.3390/diagnostics12122899.
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Biomarkers including fasting blood sugar, heart rate, electrocardiogram (ECG), blood pressure, etc. are essential in the heart disease (HD) diagnosing. Using wearable sensors, these measures are collected and applied as inputs to a deep learning (DL) model for HD diagnosis. However, it is observed that model accuracy weakens when the data gathered are scarce or imbalanced. Therefore, this work proposes two DL-based frameworks, GAN-1D-CNN, and GAN-Bi-LSTM. These frameworks contain: (1) a generative adversarial network (GAN) and (2) a one-dimensional convolutional neural network (1D-CNN) or bi-directional long short-term memory (Bi-LSTM). The GAN model is utilized to augment the small and imbalanced dataset, which is the Cleveland dataset. The 1D-CNN and Bi-LSTM models are then trained using the enlarged dataset to diagnose HD. Unlike previous works, the proposed frameworks increase the dataset first to avoid the prediction bias caused by the limited data. The GAN-1D-CNN achieved 99.1% accuracy, specificity, sensitivity, F1-score, and 100% area under the curve (AUC). Similarly, the GAN-Bi-LSTM obtained 99.3% accuracy, 99.2% specificity, 99.3% sensitivity, 99.2% F1-score, and 100% AUC. Furthermore, time complexity of proposed frameworks is investigated with and without principal component analysis (PCA). The PCA method reduced prediction times for 61 samples using GAN-1D-CNN and GAN-Bi-LSTM to 68.8 and 74.8 ms, respectively. These results show that it is reliable to use our frameworks for augmenting limited data and predicting heart disease.
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Zhang, Chuan, Min Yi, Fawang Ye, Qingjun Xu, Xinchun Li, and Qingqing Gan. "Application and Evaluation of Deep Neural Networks for Airborne Hyperspectral Remote Sensing Mineral Mapping: A Case Study of the Baiyanghe Uranium Deposit in Northwestern Xinjiang, China." Remote Sensing 14, no. 20 (October 13, 2022): 5122. http://dx.doi.org/10.3390/rs14205122.
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Deep learning is a popular topic in machine learning and artificial intelligence research and has achieved remarkable results in various fields. In geological remote sensing, mineral mapping is an appealing application of hyperspectral remote sensing for geological surveyors. Whether deep learning can improve the mineral identification ability in hyperspectral remote sensing images, especially for the discrimination of spectrally similar and intimately mixed minerals, needs to be evaluated. In this study, shortwave airborne spectrographic imager (SASI) hyperspectral images of the Baiyanghe uranium deposit in Northwestern Xinjiang, China, were used as experimental data. Three deep neural network (DNN) models were designed: a fully connected neural network (FCNN), a one-dimensional convolutional neural network (1D CNN), and a one-dimensional and two-dimensional convolutional neural network (1D and 2D CNN). A sample dataset containing five minerals was constructed for model training and validation, which was divided into training, validation and test sets at a ratio of 6:2:2. The final test accuracies of the FCNN, 1D CNN, and 1D and 2D CNN were 91.24%, 93.67% and 94.77%, respectively. The three DNNs were used for mineral identification and mapping of SASI hyperspectral images of the Baiyanghe uranium mining area. The mapping results were compared with the mapping results of the support vector machine (SVM) and the mixture-tuned matched filtering (MTMF) method. Combined with the ground spectral data obtained by the spectrometer, spectral verification and interpretation were carried out on sections that the two kinds of methods identified differently. The verification results show that the mapping results of the 1D and 2D CNN were more accurate than those of the other methods. More importantly, for minerals with similar spectral characteristics, such as short-wavelength white mica and medium-wavelength white mica, the 1D and 2D CNN model had a more accurate discrimination effect than the other DNN models, indicating that the introduction of spatial information can improve the mineral identification ability in hyperspectral remote sensing images. In general, CNNs have good application prospects in geological mapping of hyperspectral remote sensing images and are worthy of further development in future work.
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Hao, Yong, Xiyan Li, Chengxiang Zhang, and Zuxiang Lei. "Online Inspection of Browning in Yali Pears Using Visible-Near Infrared Spectroscopy and Interpretable Spectrogram-Based CNN Modeling." Biosensors 13, no. 2 (January 29, 2023): 203. http://dx.doi.org/10.3390/bios13020203.
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Browning is the most common physiological disease of Yali pears during storage. At the initial stage, browning only occurs in the tissues near the fruit core and cannot be detected from the appearance. The disease, if not identified and removed in time, will seriously undermine the quality and sale of the whole batch of fruit. Therefore, there is an urgent need to explore a method for early diagnosis of the browning in Yali pears. In order to realize the dynamic and online real-time detection of the browning in Yali pears, this paper conducted online discriminant analysis on healthy Yali pears and those with different degrees of browning using visible-near infrared (Vis-NIR) spectroscopy. The experimental results show that the prediction accuracy of the original spectrum combined with a 1D-CNN deep learning model reached 100% for the test sets of browned pears and healthy pears. Features extracted by the 1D-CNN method were converted into images by Gramian angular field (GAF) for PCA visual analysis, showing that deep learning had good performance in extracting features. In conclusion, Vis-NIR spectroscopy combined with the 1D-CNN discriminant model can realize online detection of browning in Yali pears.
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Baydemir, Recep, Fatma Latifoğlu, and Fırat Orhanbulucu. "Classification Mental Workload Levels from EEG Signals with 1D Convolutional Neural Network." European Journal of Research and Development 2, no. 4 (December 31, 2022): 13–23. http://dx.doi.org/10.56038/ejrnd.v2i4.193.
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Mental workload (MWL) can be estimated according to the state of cognitive capacity after an activity. In this study, it is aimed to classify MWL levels from Electroencephalogram (EEG) signals recorded from a task moment. Using the proposed one-dimensional convolutional neural network (1D-CNN) model in the study, low (L) and high (H) level WL states were classified. The classification process was carried out in two stages. EEG signals passed through the preprocessing stage were classified with 1D-CNN in the first stage. In the second step, these signals were decomposed into subbands by applying Empirical Mode Decomposition (EMD) and classified with 1D-CNN. As a result of the classification process, accuracy (Acc), sensitivity (Sens), and specificity (Spe) values were obtained and evaluated in this study. As a result of the evaluation, the most successful Acc rate was 98.4%, Sens rate 97.62%, and Spe rate 98.94%
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Xu, Yaning, and Lei Yang. "Based on Improved CNN Bearing Fault Detection." Journal of Physics: Conference Series 2171, no. 1 (January 1, 2022): 012073. http://dx.doi.org/10.1088/1742-6596/2171/1/012073.
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Abstract In recent years, the problem about the fault detection of rolling bearings in mechanical equipment has gradually become an important research direction.Among them, the diagnostic method based on vibration signal analysis is widely used in the fault detection of rolling bearings.Since the one-dimensional convolutional neural network (1D-CNN) has certain limitations on the processing of vibration signal data, the solution to this problem in this paper is to integrate the attention mechanism and bi-directional long and short-term memory neural network (BiLSTM) on the basis of the one-dimensional convolutional neural network, using the attention mechanism to give different weights to different feature dimensions in the sample data and extract key and important information, thus the sample data can be further optimized.On the other hand, BiLSTM can automatically extract the deep information of the bearing vibration signal, which makes up for the deficiency of artificial extraction features to a certain extent, and strengthens the discriminative property of high-level features.Subsequently, the improved CNN bearing fault detection model was experimentally validated using the Case Western Reserves University bearing dataset, and it was concluded that the attention mechanism acting on the model obtained by adding BiLSTM to the 1D-CNN could achieve a fault identification accuracy of about 98.9% and the loss degree was reduced to about 0.17%, thus achieving an effective diagnosis of the fault state.
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Jiang, T., and X. J. Wang. "HYPERSPECTRAL IMAGES CLASSIFICATION BASED ON FUSION FEATURES DERIVED FROM 1D AND 2D CONVOLUTIONAL NEURAL NETWORK." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 335–41. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-335-2020.
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Abstract. In recent years, deep learning technology has been continuously developed and gradually transferred to various fields. Among them, Convolutional Neural Network (CNN), which has the ability to extract deep features of images due to its unique network structure, plays an increasingly important role in the realm of Hyperspectral images classification. This paper attempts to construct a features fusion model that combines the deep features derived from 1D-CNN and 2D-CNN, and explores the potential of features fusion model in the field of hyperspectral image classification. The experiment is based on the deep learning open source framework TensorFlow with Python3 as programming environment. Firstly, constructing multi-layer perceptron (MLP), 1D-CNN and 2DCNN models respectively, and then, using the pre-trained 1D-CNN and 2D-CNN models as feature extractors, finally, extracting features via constructing the features fusion model. The general open hyperspectral datasets (Pavia University) were selected as a test to compare classification accuracy and classification confidence among different models. The experimental results show that the features fusion model obtains higher overall accuracy (99.65%), Kappa coefficient (0.9953) and lower uncertainty for the boundary and unknown regions (3.43%) in the data set. Since features fusion model inherits the structural characteristics of 1D-CNN and 2DCNN, the complementary advantages between the models are achieved. The spectral and spatial features of hyperspectral images are fully exploited, thus getting state-of-the-art classification accuracy and generalization performance.
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Seo, Youngwook, Giyoung Kim, Jongguk Lim, Ahyeong Lee, Balgeum Kim, Jaekyung Jang, Changyeun Mo, and Moon S. Kim. "Non-Destructive Detection Pilot Study of Vegetable Organic Residues Using VNIR Hyperspectral Imaging and Deep Learning Techniques." Sensors 21, no. 9 (April 21, 2021): 2899. http://dx.doi.org/10.3390/s21092899.
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Contamination is a critical issue that affects food consumption adversely. Therefore, efficient detection and classification of food contaminants are essential to ensure food safety. This study applied a visible and near-infrared (VNIR) hyperspectral imaging technique to detect and classify organic residues on the metallic surfaces of food processing machinery. The experimental analysis was performed by diluting both potato and spinach juices to six different concentration levels using distilled water. The 3D hypercube data were acquired in the range of 400–1000 nm using a line-scan VNIR hyperspectral imaging system. Each diluted residue in the spectral domain was detected and classified using six classification methods, including a 1D convolutional neural network (CNN-1D) and five pre-processing methods. Among them, CNN-1D exhibited the highest classification accuracy, with a 0.99 and 0.98 calibration result and a 0.94 validation result for both spinach and potato residues. Therefore, in comparison with the validation accuracy of the support vector machine classifier (0.9 and 0.92 for spinach and potato, respectively), the CNN-1D technique demonstrated improved performance. Hence, the VNIR hyperspectral imaging technique with deep learning can potentially afford rapid and non-destructive detection and classification of organic residues in food facilities.
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Obeidat, Yusra, and Ali Mohammad Alqudah. "A Hybrid Lightweight 1D CNN-LSTM Architecture for Automated ECG Beat-Wise Classification." Traitement du Signal 38, no. 5 (October 31, 2021): 1281–91. http://dx.doi.org/10.18280/ts.380503.
Full textAbstract:
In this paper we have utilized a hybrid lightweight 1D deep learning model that combines convolutional neural network (CNN) and long short-term memory (LSTM) methods for accurate, fast, and automated beat-wise ECG classification. The CNN and LSTM models were designed separately to compare with the hybrid CNN-LSTM model in terms of accuracy, number of parameters, and the time required for classification. The hybrid CNN-LSTM system provides an automated deep feature extraction and classification for six ECG beats classes including Normal Sinus Rhythm (NSR), atrial fibrillation (AFIB), atrial flutter (AFL), atrial premature beat (APB), left bundle branch block (LBBB), and right bundle branch block (RBBB). The hybrid model uses the CNN blocks for deep feature extraction and selection from the ECG beat. While the LSTM layer will learn how to extract contextual time information. The results show that the proposed hybrid CNN-LSTM model achieves high accuracy and sensitivity of 98.22% and 98.23% respectively. This model is light and fast in classifying ECG beats and superior to other previously used models which makes it very suitable for embedded systems designs that can be used in clinical applications for monitoring heart diseases in faster and more efficient manner.
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Park, Tae-Hui, Da-Seul Jang, Gyeong-Min Bae, Kyung-Min Kim, and Johng-Hwa Ahn. "Selection of Input Variables and Comparison of Artificial Neural Networks and One-Dimensional Convolutional Neural Networks for Prediction of Wind Power Generation in Yeongheung Wind Power Plant." Journal of Korean Society of Environmental Engineers 43, no. 4 (April 30, 2021): 219–29. http://dx.doi.org/10.4491/ksee.2021.43.4.219.
Full textAbstract:
Objectives : In this study, deep learning models of artificial neural network (ANN) and one-dimension convolutional neural networks (1D-CNN) were compared to predict nonlinear wind power generation at Yeongheung wind power plant.Methods : The study site was Yeongheung-do, which has a 46 MW wind power plant. Hourly wind power and meteorological data from January to December 2018 were collected. After pre-processing with standardscaler, the training data were 64%, the validation data were 16%, and the test data were 20%. The optimum input variables of the model were selected using literature, and trial and error method. Rectified linear unit was used as the activation function. Hyperparameters were adjusted by trial and error method to optimized models. To compare the optimized models, the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) were used as the performance efficiency. Both ANN, and 1D-CNN were imported from the Keras library, and all of the performance efficiency was imported from the Scikit-learn library.Results and Discussion : The optimized input variables in this study were wind speed, wind direction, temperature, and humidity. The optimized ANN performance was R2=0.848, MAE=1.054, and RMSE=1.616, and the hyperparameters were 8 hidden layers with 100 nodes in each layer. The optimized 1D-CNN (R2=0.875, MAE=0.982, and RMSE=1.583) had 4 convolutional layers and the number of filters were 64, 128, 64, and 32 in order from the first layer, and one hidden fully connected layer had 100 nodes. The 1D-CNN had higher R2, and lower MAE and RMSE than the ANN. Therefore, the 1D-CNN was selected as the optimized model to predict wind generation of the Yeongheung wind power plant.Conclusions : The optimized 1D-CNN model in this study was more effective in predicting the Yeongheung wind power plant than the ANN. The optimal input variables were wind speed, wind direction, temperature, and humidity.