Relevant bibliographies by topics / Dimensional Convolutional Neural Network (1D-CNN)

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Dimensional Convolutional Neural Network (1D-CNN)'

Author: Grafiati
Published: 7 June 2025
Last updated: 26 June 2025

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Journal articles on the topic "Dimensional Convolutional Neural Network (1D-CNN)"

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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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Gao, Wenqiang, Zhiyun Xiao, and Tengfei Bao. "Detection and Identification of Potato-Typical Diseases Based on Multidimensional Fusion Atrous-CNN and Hyperspectral Data." Applied Sciences 13, no. 8 (2023): 5023. http://dx.doi.org/10.3390/app13085023.

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As one of the world’s most crucial crops, the potato is an essential source of nutrition for human activities. However, several diseases pose a severe threat to the yield and quality of potatoes. Timely and accurate detection and identification of potato diseases are of great importance. Hyperspectral imaging has emerged as an essential tool that provides rich spectral and spatial distribution information and has been widely used in potato disease detection and identification. Nevertheless, the accuracy of prediction is often low when processing hyperspectral data using a one-dimensional convolutional neural network (1D-CNN). Additionally, conventional three-dimensional convolutional neural networks (3D-CNN) often require high hardware consumption while processing hyperspectral data. In this paper, we propose an Atrous-CNN network structure that fuses multiple dimensions to address these problems. The proposed structure combines the spectral information extracted by 1D-CNN, the spatial information extracted by 2D-CNN, and the spatial spectrum information extracted by 3D-CNN. To enhance the perceptual field of the convolution kernel and reduce the loss of hyperspectral data, null convolution is utilized in 1D-CNN and 2D-CNN to extract data features. We tested the proposed structure on three real-world potato diseases and achieved recognition accuracy of up to 0.9987. The algorithm presented in this paper effectively extracts hyperspectral data feature information using three different dimensional CNNs, leading to higher recognition accuracy and reduced hardware consumption. Therefore, it is feasible to use the 1D-CNN network and hyperspectral image technology for potato plant disease identification.
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Pavitha N, Et al. "Adaptive One-Dimensional Convolutional Neural Network for Tabular Data." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 2231–35. http://dx.doi.org/10.17762/ijritcc.v11i9.9228.

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This study introduces an innovative approach for tackling the credit risk prediction problem using an Adaptive One-Dimensional Convolutional Neural Network (1D CNN). The proposed methodology is designed for one-dimensional data, such as tabular data, through a combination of feed-forward and back-propagation phases. During the feed-forward phase, neuron outputs are computed by applying convolution operations to previous layer outputs, along with bias terms and activation functions. The subsequent back-propagation phase updates weights and biases to minimize prediction errors. A custom weight initialization algorithm tailored to Leaky ReLU activation is employed to enhance model adaptability. The core of the proposed algorithm lies in its ability to process each training data sample across layers, optimizing weights and biases to achieve accurate predictions. Comprehensive evaluations are conducted on various machine learning algorithms, including Gaussian Naive Bayes, Logistic Regression, ensemble methods, and neural networks. The proposed Adaptive 1D CNN emerges as the top performer, consistently surpassing other methods in precision, recall, F1-score, and accuracy. This success is attributed to its specialized weight initialization, effective back-propagation, and integration of 1D convolutional layers.
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Bai, Maoyang, Peihao Peng, Shiqi Zhang, et al. "Mountain Forest Type Classification Based on One-Dimensional Convolutional Neural Network." Forests 14, no. 9 (2023): 1823. http://dx.doi.org/10.3390/f14091823.

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Convolutional neural networks (CNNs) have demonstrated their efficacy in remote sensing applications for mountain forest classification. However, two-dimensional convolutional neural networks (2D CNNs) require a significant manual involvement in the visual interpretation to obtain continuous polygon label data. To reduce the errors associated with manual visual interpretation and enhance classification efficiency, it is imperative to explore alternative approaches. In this research, we introduce a novel one-dimensional convolutional neural network (1D CNN) methodology that directly leverages field investigation data as labels for classifying mountain forest types based on multiple remote sensing data sources. The hyperparameters were optimised using an orthogonal table, and the model’s performance was evaluated on Mount Emei of Sichuan Province. Comparative assessments with traditional classification methods, namely, a random forest (RF) and a support vector machine (SVM), revealed superior results obtained by the proposed 1D CNN. Forest type classification using the 1D CNN achieved an impressive overall accuracy (OA) of 97.41% and a kappa coefficient (Kappa) of 0.9673, outperforming the U-Net (OA: 94.45%, Kappa: 0.9239), RF (OA: 88.99%, Kappa: 0.8488), and SVM (OA: 88.79%, Kappa: 0.8476). Moreover, the 1D CNN model was retrained using limited field investigation data from Mount Wawu in Sichuan Province and successfully classified forest types in that region, thereby demonstrating its spatial-scale transferability with an OA of 90.86% and a Kappa of 0.8879. These findings underscore the effectiveness of the proposed 1D CNN in utilising multiple remote sensing data sources for accurate mountain forest type classification. In summary, the introduced 1D CNN presents a novel, efficient, and reliable method for mountain forest type classification, offering substantial contributions to the field.
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Nourmohammadi, Farzaneh, Chetan Parmar, Elmar Wings, and Jaume Comellas. "Using Convolutional Neural Networks for Blocking Prediction in Elastic Optical Networks." Applied Sciences 14, no. 5 (2024): 2003. http://dx.doi.org/10.3390/app14052003.

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This paper presents a study on connection-blocking prediction in Elastic Optical Networks (EONs) using Convolutional Neural Networks (CNNs). In EONs, connections are established and torn down dynamically to fulfill the instantaneous requirements of the users. The dynamic allocation of the connections may cause spectrum fragmentation and lead to network performance degradation as connection blocking increases. Predicting potential blocking situations can be helpful during EON operations. For example, this prediction could be used in real networks to trigger proper spectrum defragmentation mechanisms at suitable moments, thereby enhancing network performance. Extensive simulations over the well-known NSFNET (National Science Foundation Network) backbone network topology were run by generating realistic traffic patterns. The obtained results are later used to train the developed machine learning models, which allow the prediction of connection-blocking events. Resource use was continuously monitored and recorded during the process. Two different Convolutional Neural Network models, a 1D CNN (One-Dimensional Convolutional Neural Network) and 2D CNN (Two-Dimensional Convolutional Neural Network), are proposed as the predicting methods, and their behavior is compared to other conventional models based on an SVM (Support Vector Machine) and KNN (K Nearest Neighbors). The results obtained show that the proposed 2D CNN predicts blocking with the best accuracy (92.17%), followed by the SVM, the proposed 1D CNN, and KNN. Results suggest that 2D CNN can be helpful in blocking prediction and might contribute to increasing the efficiency of future EON networks.
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Liu, Bingxin, Ying Li, Guannan Li, and Anling Liu. "A Spectral Feature Based Convolutional Neural Network for Classification of Sea Surface Oil Spill." ISPRS International Journal of Geo-Information 8, no. 4 (2019): 160. http://dx.doi.org/10.3390/ijgi8040160.

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Spectral characteristics play an important role in the classification of oil film, but the presence of too many bands can lead to information redundancy and reduced classification accuracy. In this study, a classification model that combines spectral indices-based band selection (SIs) and one-dimensional convolutional neural networks was proposed to realize automatic oil films classification using hyperspectral remote sensing images. Additionally, for comparison, the minimum Redundancy Maximum Relevance (mRMR) was tested for reducing the number of bands. The support vector machine (SVM), random forest (RF), and Hu’s convolutional neural networks (CNN) were trained and tested. The results show that the accuracy of classifications through the one dimensional convolutional neural network (1D CNN) models surpassed the accuracy of other machine learning algorithms such as SVM and RF. The model of SIs+1D CNN could produce a relatively higher accuracy oil film distribution map within less time than other models.
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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 (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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Peyghambari, Sima, Yun Zhang, Hassan Heidarian, and Milad Sekandari. "One-Dimensional-Mixed Convolution Neural Network and Covariance Pooling Model for Mineral Mapping of Porphyry Copper Deposit Using PRISMA Hyperspectral Data." Photogrammetric Engineering & Remote Sensing 90, no. 8 (2024): 511–22. http://dx.doi.org/10.14358/pers.24-00006r2.

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Mapping distribution of alterations around porphyry copper deposits (PCDs) greatly affects mineral exploration. Diverse geological processes generate irregular alteration patterns with diverse spectral characteristics in mineral deposits. Applying remotely sensed hyperspectral images (HSIs) is an appealing technology for geologic surveyors to generate alteration maps. Conventional methods mainly use shallow spectral absorption features to discriminate minerals and cannot extract their important spectral information. Deep neural networks with nonlinear layers can evoke the deep spectral and spatial information of HSIs. Deep learning???based methods include fully connected neural networks, convolutional neural networks, and hybrid convolutional networks like mixed convolution neural network and covariance pooling (MCNN‐CP) algorithms. However, each has its advantages and limitations. To significantly avoid losing important spectral features, we proposed a new method by fusing a one‐dimensional convolutional neural network (1D‐CNN) with MCNN‐CP (1D‐MCNN‐CP), achieving an overall accuracy (97.44%) of mineral mapping from PRISMA HSIs. This research deduced that 1D‐MCNN‐CP improved performance and reduced misclassification errors among minerals sharing similar spectral features.
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Chen, Chih-Cheng, Zhen Liu, Guangsong Yang, Chia-Chun Wu, and Qiubo Ye. "An Improved Fault Diagnosis Using 1D-Convolutional Neural Network Model." Electronics 10, no. 1 (2020): 59. http://dx.doi.org/10.3390/electronics10010059.

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The diagnosis of a rolling bearing for monitoring its status is critical in maintaining industrial equipment while using rolling bearings. The traditional method of diagnosing faults of the rolling bearing has low identification accuracy, which needs artificial feature extraction in order to enhance the accuracy. The one-dimensional convolution neural network (1D-CNN) method can not only diagnose bearing faults accurately, but also overcome shortcomings of the traditional methods. Different from machine learning and other deep learning models, the 1D-CNN method does not need pre-processing one-dimensional data of rolling bearing’s vibration. In this paper, the 1D-CNN network architecture is proposed in order to effectively improve the accuracy of the diagnosis of rolling bearing, and the number of convolution kernels decreases with the reduction of the convolution kernel size. The method obtains high accuracy and improves the generalizing ability by introducing the dropout operation. The experimental results show 99.2% of the average accuracy under a single load and 98.83% under different loads.
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Han, Yudong, Zhaobo Li, and Jiaqi Li. "Pavement condition detection using acceleration data collected by smartphones based on 1D convolutional neural network." Journal of the Croatian Association of Civil Engineers 76, no. 11 (2024): 979–91. https://doi.org/10.14256/jce.3958.2024.

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Vibration-based pavement condition detection methods have advanced in recent years, and it has been proven to be feasible to identify pavement conditions by analysing acceleration data. In this study, a public participation solution is proposed, and a one-dimensional convolutional neural network (1D-CNN) is introduced to directly process acceleration signals, addressing the limitations of traditional machine-learning classification methods. In this study, a smartphone and bicycle were used as the experimental tools, and 422 samples of acceleration data across the X-, Y-, and Z-axes were collected, including four types of pavement conditions: bumpy pavement, speed bumps, smooth pavement, and potholes. Five types of 1D-CNN with different activation functions and network structures were designed to classify the data and were compared with machine learning algorithms, including support vector machine (SVM) and radial basis function (RBF) neural networks. The results show that a 1D-CNN, with three convolution layers and three pooling layers using the ReLU activation function, achieved the best classification performance, with a classification accuracy of 0.9976. Compared with SVM and RBF neural networks, CNN not only saves considerable time by eliminating manual feature extraction operations but also provides higher classification accuracy.
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Dissertations / Theses on the topic "Dimensional Convolutional Neural Network (1D-CNN)"

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Ghibellini, Alessandro. "Trend prediction in financial time series: a model and a software framework." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24708/.

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The research has the aim to build an autonomous support for traders which in future can be translated in an Active ETF. My thesis work is characterized for a huge focus on problem formulation and an accurate analysis on the impact of the input and the length of the future horizon on the results. I will demonstrate that using financial indicators already used by professional traders every day and considering a correct length of the future horizon, it is possible to reach interesting scores in the forecast of future market states, considering both accuracy, which is around 90% in all the experiments, and confusion matrices which confirm the good accuracy scores, without an expensive Deep Learning approach. In particular, I used a 1D CNN. I also emphasize that classification appears to be the best approach to address this type of prediction in combination with proper management of unbalanced class weights. In fact, it is standard having a problem of unbalanced class weights, otherwise the model will react for inconsistent trend movements. Finally I proposed a Framework which can be used also for other fields which allows to exploit the presence of the Experts of the sector and combining this information with ML/DL approaches.
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Bosello, Michael. "Integrating BDI and Reinforcement Learning: the Case Study of Autonomous Driving." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21467/.

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Recent breakthroughs in machine learning are paving the way to the vision of software 2.0 era, which foresees the replacement of traditional software development with such techniques for many applications. In the context of agent-oriented programming, we believe that mixing together cognitive architectures like the BDI one and learning techniques could trigger new interesting scenarios. In that view, our previous work presents Jason-RL, a framework that integrates BDI agents and Reinforcement Learning (RL) more deeply than what has been already proposed so far in the literature. The framework allows the development of BDI agents having both explicitly programmed plans and plans learned by the agent using RL. The two kinds of plans are seamlessly integrated and can be used without differences. Here, we take autonomous driving as a case study to verify the advantages of the proposed approach and framework. The BDI agent has hard-coded plans that define high-level directions while fine-grained navigation is learned by trial and error. This approach – compared to plain RL – is encouraging as RL struggles in temporally extended planning. We defined and trained an agent able to drive in a track with an intersection, at which it has to choose the correct path to reach the assigned target. A first step towards porting the system in the real-world has been done by building a 1/10 scale racecar prototype which learned how to drive in a simple track.
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Book chapters on the topic "Dimensional Convolutional Neural Network (1D-CNN)"

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Rostovski, Jakob, Mohammad Hasan Ahmadilivani, Andrei Krivošei, Alar Kuusik, and Muhammad Mahtab Alam. "Real-Time Gait Anomaly Detection Using 1D-CNN and LSTM." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-59091-7_17.

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AbstractAnomaly detection and fall prevention represent one of the key research areas within gait analysis for patients suffering from neurological disorders. Deep Learning has penetrated into healthcare applications, encompassing disease diagnosis and anomaly prediction. Connected wearable medical sensors are emerging due to computationally expensive machine learning tasks, which traditionally require use of remote PC or cloud computing. However, to reduce needs for wireless communication channel throughput, for data processing latency, and increase service reliability and safety, on device machine learning is gaining attention. This paper presents an innovative approach that leverages one dimensional convolutional neural network (1D-CNN) and long-short term memory (LSTM) neural network for the real-time detection of abnormal gait patterns during the step. Real-time anomaly detection pertains to the algorithm’s ability to promptly detect true gait abnormality occurrence during the swing phase of an ongoing step.For the experiments, we have collected eight different common gait anomalies, simulated by 22 persons, using motion sensors containing multidimensional inertial measurement units (IMUs).Results have demonstrated that the proposed 1D-CNN-AD algorithm achieves an average accuracy of 95% and an average F1-score of 88% for all gait types and can run in true real-time. Average earliness for 1D-CNN-AD algorithm was 0.6 s, which is mid-swing phase of the step. Proposed LSTM-AD algorithm achieved average accuracy of 87% and average F1-score of 70% for all gait types.
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Kaur, Sukhpreet, and Nilima Kulkarni. "Dimensional Emotion Recognition Using EEG Signals via 1D Convolutional Neural Network." In Third Congress on Intelligent Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9225-4_46.

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Fitri, Nurul Aulia, Yunendah Nur Fu’adah, and Rita Magdalena. "Fetal ECG Signal Processing Using One-Dimensional Convolutional Neural Network (1D CNN) for Fetal Arrhythmias Detection." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0248-4_6.

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Montesinos López, Osval Antonio, Abelardo Montesinos López, and Jose Crossa. "Convolutional Neural Networks." In Multivariate Statistical Machine Learning Methods for Genomic Prediction. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89010-0_13.

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AbstractWe provide the fundamentals of convolutional neural networks (CNNs) and include several examples using the Keras library. We give a formal motivation for using CNN that clearly shows the advantages of this topology compared to feedforward networks for processing images. Several practical examples with plant breeding data are provided using CNNs under two scenarios: (a) one-dimensional input data and (b) two-dimensional input data. The examples also illustrate how to tune the hyperparameters to be able to increase the probability of a successful application. Finally, we give comments on the advantages and disadvantages of deep neural networks in general as compared with many other statistical machine learning methodologies.
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Yabuki, Nobuyoshi, Tomohiro Fukuda, and Ryu Izutsu. "As-Built Detection of Structures by the Segmentation of Three-Dimensional Models and Point Cloud Data." In CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality. Firenze University Press, 2023. http://dx.doi.org/10.36253/10.36253/979-12-215-0289-3.111.

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At construction sites, as-built management is generally conducted by taking pictures or surveying with total stations and comparing the images or survey data with design drawings or Building Information Modeling (BIM) models. Since this work is time-consuming and error-prone, more efficient and accurate methods using advanced Information and Communication Technology (ICT) are desired. Therefore, this research proposes a method that can efficiently capture the progress of construction by detecting each constructed structural member, such as beams, columns, connections, etc. In this proposed method, construction engineers first take many pictures of the construction site and conduct automatic image segmentation using a pre-trained Convolutional Neural Network (CNN) model. Next, point cloud data is generated from taken pictures by using Structure from Motion (SfM). Then, the point cloud data is semantically segmented by overlapping the segmented images and point cloud data using the pin-hole camera technique. Finally, the design BIM model and segmented point cloud data are overlapped, and constructed parts of the BIM model can be detected, which can be reported as as-built parts. A prototype system was developed and applied to an actual railway construction project in Osaka, Japan for testing the accuracy and performance of the system
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Yabuki, Nobuyoshi, Tomohiro Fukuda, and Ryu Izutsu. "As-Built Detection of Structures by the Segmentation of Three-Dimensional Models and Point Cloud Data." In CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality. Firenze University Press, 2023. http://dx.doi.org/10.36253/979-12-215-0289-3.111.

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At construction sites, as-built management is generally conducted by taking pictures or surveying with total stations and comparing the images or survey data with design drawings or Building Information Modeling (BIM) models. Since this work is time-consuming and error-prone, more efficient and accurate methods using advanced Information and Communication Technology (ICT) are desired. Therefore, this research proposes a method that can efficiently capture the progress of construction by detecting each constructed structural member, such as beams, columns, connections, etc. In this proposed method, construction engineers first take many pictures of the construction site and conduct automatic image segmentation using a pre-trained Convolutional Neural Network (CNN) model. Next, point cloud data is generated from taken pictures by using Structure from Motion (SfM). Then, the point cloud data is semantically segmented by overlapping the segmented images and point cloud data using the pin-hole camera technique. Finally, the design BIM model and segmented point cloud data are overlapped, and constructed parts of the BIM model can be detected, which can be reported as as-built parts. A prototype system was developed and applied to an actual railway construction project in Osaka, Japan for testing the accuracy and performance of the system
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Vijaya Saraswathi, R., Suraj Sanganbhatla, R. Aditya Vardhan Reddy, Srigiri Shashank Kumar, and Punnamaraju J. V. G. K. Pranav. "IPUDCRNN: Integrated Phished URL Detection Using Convolutional Recurrent Neural Network—1D CNN + LSTM." In Smart Innovation, Systems and Technologies. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5081-8_32.

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Paredes, Jimmy, Erick Cuenca, Claudio Coloma, and Daniel Grimaldi. "Hate Speech Detection During the 2023 Chilean Plebiscite Constitutional Reform." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-87065-1_5.

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Abstract This work presents a classification of hate speech using Natural Language Processing approaches, including collecting and labeling the data, text augmentation using the back-translation technique to address the imbalanced class problem, and data preprocessing. This led to the creation of a model capable of classifying hate in tweets in Spanish from platform X in the context of the 2023 Chilean Constitutional Plebiscite. Results show that approaches based on Convolutional Neural Networks (CNNs) in 1 dimension obtained better results than approaches based on Machine Learning because CNNs can identify patterns and relations between consecutive words, making them more accurate in understanding the context of the tweet. The CNN model achieved an overall accuracy of 86% on the testing dataset, while Machine Learning approaches achieved between 79% and 81% on the testing dataset. It is essential to consider that since the dataset presents an imbalance in the classes, other metrics were also presented, such as precision, F1-score, and recall, where, once again, the best results were obtained using CNN.
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Dutta, Dipankar, Soumya Porel, Debabrata Tah, and Paramartha Dutta. "One-Dimensional Convolutional Neural Network for Data Classification." In Advanced Technologies for Realizing Sustainable Development Goals: 5G, AI, Big Data, Blockchain, and Industry 4.0 Application. BENTHAM SCIENCE PUBLISHERS, 2024. http://dx.doi.org/10.2174/9789815256680124010006.

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CNN has emerged as the de-facto standard for several machine learning (ML) and computer vision applications. It is known for its classification and feature extraction capabilities. Many ML techniques require separate handcrafted feature extraction steps before classification, which are “sub-optimal” in nature. Unlike these, CNN extracts “optimal” features directly from raw data, enabling it to enhance classification accuracy. Two-dimensional CNN (2D-CNN) is the most common one, where inputs to the CNNs are 2D in nature, such as images. Here, we used 1D-CNN for data classification as we used 1D inputs. 1D-CNN has lower computational complexity than 2D-CNN. Mainly for this, we preferred 1D-CNN over 2D-CNN. To demonstrate the superiority of the proposed generic classifier, we compared its classification accuracies with several other generic classifiers. We used 21 benchmark data sets from the UCI machine learning repository to achieve this. Tests prove the superiority of the proposed 1D-CNN-based generic classifier. Many 1D-CNN-based application-specific classifiers are proposed in the literature, but the proposed classifier is applicable for many types of tabular data i.e., it is a generic classifier.
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Naik, K. Jairam, and Annukriti Soni. "Video Classification Using 3D Convolutional Neural Network." In Advancements in Security and Privacy Initiatives for Multimedia Images. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-2795-5.ch001.

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Since video includes both temporal and spatial features, it has become a fascinating classification problem. Each frame within a video holds important information called spatial information, as does the context of that frame relative to the frames before it in time called temporal information. Several methods have been invented for video classification, but each one is suffering from its own drawback. One of such method is called convolutional neural networks (CNN) model. It is a category of deep learning neural network model that can turn directly on the underdone inputs. However, such models are recently limited to handling two-dimensional inputs only. This chapter implements a three-dimensional convolutional neural networks (CNN) model for video classification to analyse the classification accuracy gained using the 3D CNN model. The 3D convolutional networks are preferred for video classification since they inherently apply convolutions in the 3D space.
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Conference papers on the topic "Dimensional Convolutional Neural Network (1D-CNN)"

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Saida, Taisei, and Mayuko Nishio. "Seismic Fragility Assessment using Explainable Deep Kernel Learning Surrogate Model considering Structural and Seismic Uncertainties." In IABSE Symposium, Tokyo 2025: Environmentally Friendly Technologies and Structures: Focusing on Sustainable Approaches. International Association for Bridge and Structural Engineering (IABSE), 2025. https://doi.org/10.2749/tokyo.2025.2581.

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<p>This study proposes a surrogate model using deep kernel learning (DKL) with a convolutional neural network (CNN) and attention mechanism for efficient seismic fragility assessment of infrastructure. The CNN extracts features from seismic response spectrum, enabling efficient Gaussian process (GP) regression in a lower-dimensional space. An automatic relevance determination (ARD) kernel and attention mechanism enhance explainability by evaluating input variable contributions and attention weights for response spectrum. The model achieved high prediction accuracy, outperforming GPs, especially with limited data. Fragility analysis using the surrogate model reduced computational cost to 0.05% of direct numerical simulation.</p>
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Peng, Wei, Ruimin Jie, Bo Liu, Lingmei Ma, and Chen Zhu. "High-performance Raman-based Distributed Temperature Sensing Empowered by Data-Driven Dual-Stage 1D-CNN." In CLEO: Applications and Technology. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/cleo_at.2024.jtu2a.204.

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With the assistance of a data-driven dual-stage convolutional neural network model, we have experimentally demonstrated a high-performance Raman-based distributed temperature sensing system with an update rate of 0.02 s and a temperature uncertainty of 0.09°C.
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Jia, Zhenge, Zhepeng Wang, Feng Hong, Lichuan PING, Yiyu Shi, and Jingtong Hu. "Learning to Learn Personalized Neural Network for Ventricular Arrhythmias Detection on Intracardiac EGMs." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/359.

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Life-threatening ventricular arrhythmias (VAs) detection on intracardiac electrograms (IEGMs) is essential to Implantable Cardioverter Defibrillators (ICDs). However, current VAs detection methods count on a variety of heuristic detection criteria, and require frequent manual interventions to personalize criteria parameters for each patient to achieve accurate detection. In this work, we propose a one-dimensional convolutional neural network (1D-CNN) based life-threatening VAs detection on IEGMs. The network architecture is elaborately designed to satisfy the extreme resource constraints of the ICD while maintaining high detection accuracy. We further propose a meta-learning algorithm with a novel patient-wise training tasks formatting strategy to personalize the 1D-CNN. The algorithm generates a well-generalized model initialization containing across-patient knowledge, and performs a quick adaptation of the model to the specific patient's IEGMs. In this way, a new patient could be immediately assigned with personalized 1D-CNN model parameters using limited input data. Compared with the conventional VAs detection method, the proposed method achieves 2.2% increased sensitivity for detecting VAs rhythm and 8.6% increased specificity for non-VAs rhythm.
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Wang, Shuqing, and Yufeng Jiang. "Neural Network-Based Method for Structural Damage and Scour Estimation Using Modal Parameters and Dynamic Responses." In ASME 2020 39th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/omae2020-18461.

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Abstract Wind energy is the most promising clean, renewable energies to the power industry in the world. More and more wind turbine structures equipped with the larger capacity, taller towers, and longer blades were installed at the offshore/onshore wind farms. But these structures face many harsh environmental conditions, and structural damage and foundation scour are continuously accumulated. It could alter the modal parameter and dynamic response and further reduce the safety of structures. It is a significant challenge on how to accurately estimate the structural states if there is structural damage or foundation scour. For addressing these limitations, a One Dimensional Convolutional Neural Network (1D CNN) method is developed to estimate the structural state. After the Fast Fourier Transform of the acceleration signals, these frequency responses are used as the input to train the 1D CNN, while these states are estimated as the output. A simplified spring-beam model is introduced to simulate the pile-soil interaction, and the effects of the damage and scour on natural frequencies are investigated and compared. The effectiveness and robustness of the proposed 1D CNN method have been numerically investigated by several scenarios associated with the wind turbine structure. Results demonstrate that the 1D CNN method can accurately estimate the structural states, even under a noisy environment. Further, the 1D CNN method can identify the location of damage and scour depth with very high accuracy. This approach may be useful in the on-site structural health monitoring in the wind turbine structure.
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Bandeira, Jonathan da Silva, and Roberta Andrade de Araújo Fagundes. "Enhancing Alzheimer’s Disease Diagnosis: Insights from MLP and 1D CNN Models." In Simpósio Brasileiro de Sistemas de Informação. Sociedade Brasileira de Computação, 2025. https://doi.org/10.5753/sbsi.2025.246492.

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Context: Alzheimer’s Disease (AD) is a complex neurodegenerative disorder that requires early diagnosis to improve patient outcomes. Recent advances in computational intelligence have sparked interest in leveraging machine learning to enhance diagnostic accuracy and efficiency. These innovations are crucial for transforming decision-making within Information Systems in clinical settings. Problem: Traditional methods like PET-scans and cerebrospinal fluid collection are highly accurate but costly and invasive, limiting accessibility. Developing data-driven, non-invasive solutions that retain diagnostic accuracy while handling complex biomedical data, such as plasma protein concentrations, remains a challenge. Solution: This study utilizes neural networks, specifically Multi-Layer Perceptron (MLP) and One-Dimensional Convolutional Neural Network (1D CNN). Preprocessing included Recursive Feature Elimination (RFE) for feature selection and Synthetic Minority Oversampling Technique (SMOTE) for data augmentation, addressing class imbalance. SI Theory: Grounded in Complexity Theory, the study examines how machine learning models can enhance data-driven medical systems by efficiently managing critical, highly sensitive datasets. Method: An experimental quantitative approach was used to evaluate binary and multiclass classifiers on a dataset with 120 protein features from 259 patients. Summary of Results: The MLP exhibited strong performance in specific subsets, achieving superior metrics in the binary classification after feature selection and data augmentation. Meanwhile, the 1D CNN excelled in multiclass classification, leveraging its convolutional layers to extract critical features from subtle protein variations, improving accuracy and robustness. Contributions and Impact on IS Field: This research enhances medical information systems by proposing machine learning models that can be integrated for accurate diagnostics, supporting clinical decision-making and advancing healthcare practices.
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Pastore, Thomas Sponchiado, Gabriel de Oliveira Ramos, and Jean Schmith. "Cardiac pathology classification with one-dimensional convolutional neural network." In Simpósio Brasileiro de Computação Aplicada à Saúde. Sociedade Brasileira de Computação - SBC, 2025. https://doi.org/10.5753/sbcas.2025.7008.

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Currently, technology is indispensable in the medical field and the use of artificial intelligence tools is responsible for accelerating several processes, facilitating data acquisition, and recognizing important patterns for patient diagnosis. Focusing on cardiac pathologies, the electrocardiogram is the most widely used examination to diagnose patients, and optimizing this process is extremely important. Therefore, the objective of this study is to develop a model capable of classifying cardiac pathologies using raw ECG signals. For this purpose, signals from the PTB-XL database, recorded in the 12-lead standard, were used to train a 1D convolutional neural network without pre-training. Various hyperparameters were adjusted to find the model that is the best suited to the application. The model was evaluated using a test dataset, achieving an accuracy of 84%. The model demonstrated satisfactory performance, which can lead to the possibility of improving the current diagnostic system by accelerating the examination reading process and helping healthcare professionals interpret the results.
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Shen, Dan, Carolyn Sheaff, Mengqing Guo, Erik Blasch, Khanh D. Pham, and Genshe Chen. "Three-dimensional convolutional neural network (3D-CNN) for satellite behavior discovery." In Sensors and Systems for Space Applications XIV, edited by Khanh D. Pham and Genshe Chen. SPIE, 2021. http://dx.doi.org/10.1117/12.2589044.

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Sarwar, Muhammad Ali, Nayab Hassan, and Hammad Hassan. "Weed Classification Using a Two-Dimensional Deep Convolutional Neural Network (CNN)." In 2023 International Conference on Frontiers of Information Technology (FIT). IEEE, 2023. http://dx.doi.org/10.1109/fit60620.2023.00031.

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Gangopadhyay, Tryambak, Anthony Locurto, Paige Boor, James B. Michael, and Soumik Sarkar. "Characterizing Combustion Instability Using Deep Convolutional Neural Network." In ASME 2018 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/dscc2018-9208.

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Detecting the transition to an impending instability is important to initiate effective control in a combustion system. As one of the early applications of characterizing thermoacoustic instability using Deep Neural Networks, we train our proposed deep convolutional neural network (CNN) model on sequential image frames extracted from hi-speed flame videos by inducing instability in the system following a particular protocol — varying the acoustic length. We leverage the sound pressure data to define a non-dimensional instability measure used for applying an inexpensive but noisy labeling technique to train our supervised 2D CNN model. We attempt to detect the onset of instability in a transient dataset where instability is induced by a different protocol. With the continuous variation of the control parameter, we can successfully detect the critical transition to a state of high combustion instability demonstrating the robustness of our proposed detection framework, which is independent of the combustion inducing protocol.
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Li, Dexuan, Zhiming Chen, and Sepehrnoori Kamy. "An Efficient Approach for Automatic Parameter Inversion Based on Deep Reinforcement Learning." In SPE Annual Technical Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/214782-ms.

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Summary Parameter evaluations are the first and primary tasks to understand the natural gas hydrate reservoirs. However, there still lacks some effective means for parameter evaluations in hydrate reservoirs. To improve this situation, this paper tries to combine the well testing with deep learning (DL) method for solving parameter inversion problems of natural gas hydrate wells. First, a radially-composite well testing model with dynamic interface is developed to represent the hydrate dissociation driven by depressurization. Then, by Laplace transform, the wellbore pressure is solved and adopted to train a one-dimensional convolutional neural network (1D CNN) and the optimal convolutional neural network (CNN) is obtained by minimizing mean square error. In the CNN, the wellbore pressure is used as input of the network after nondimensionalization, and the interpreted parameters are permeability, wellbore storage coefficient, skin factor and dissociation factor. Finally, the well testing and DL method is verified and applied in a field case. Results show that the sensitivity of the parameter on pressure transient behavior will affect the accuracy of parameter inversion. The 1D CNN is tested with synthetic data, which shows great practicality and high accuracy of curve matching. During the field application, when compared with manual match, the relative errors of wellbore storage coefficient and dissociation factor by the proposed method are 4.863% and 1.933%, respectively. The proposed well testing and DL method is proven to be suitable for problem inversion of natural gas hydrate wells, which may provide a new tool for engineers to understand the natural gas hydrate reservoirs.
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