Acoustic Model with Multiple Lexicon Types for Indonesian Speech Recognition by Taufik Fuadi Abidin, Alim Misbullah, Ridha Ferdhiana, Laina Farsiah, Muammar Zikri Aksana, Hammam Riza

“…Utilizing the four lexicon types and increasing the data through augmentation to train the acoustic models can lower the word error rate percentage in the GMM-HMM, TDNN factorization (TDNNF), and CNN-TDNNF-augmented models to 40.85%, 24.96%, and 19.03%, respectively.…”
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Recognition of fabric composition of clothing in an image in e-commerce using neural networks by V. V. Sorokina

“…Development of new approach for recognizing the fabric composition of clothing in e-commerce images by using generative adversarial network(GAN) to generate synthetic images of clothing with known fabric composition, to be used to train the CNN to classify the fabric composition of real clothing images. …”
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Dataset for developing deep learning models to assess crack width and self-healing progress in concrete by Jacek Jakubowski, Kamil Tomczak

“…The considerable number of brightness profiles coupled with manual reference measurements make the dataset well suited for developing an image-based deep CNN models or analytic algorithms for assessing crack widths in concrete. …”
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Exploring Coevolution of Emotional Contagion and Behavior for Microblog Sentiment Analysis: A Deep Learning Architecture by Qi Zhang, Zufan Zhang, Maobin Yang, Lianxiang Zhu

“…Secondly, in the weighted network, the Deepwalk algorithm is used to build the sequence representation of microblogs which are similar to the target microblog. Next, a CNN-BiLSTM-Attention network (the convolutional neural network and bidirectional long short-term memory network with a multihead attention mechanism) is designed to analyze the sentiment analysis of target and similar microblogs. …”
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Effectiveness of the Spatial Domain Techniques in Digital Image Steganography by Rosshini Selvamani, Yusliza Yusoff

“…In addition to using statistics as a foundation, convolution neural networks (CNN), generative adversarial networks (GAN), coverless approaches, and machine learning are all used to construct steganographic methods. …”
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An Airborne Gravity Gradient Compensation Method Based on Convolutional and Long Short-Term Memory Neural Networks by Shuai Zhou, Changcheng Yang, Yi Cheng, Jian Jiao

“…This paper proposes a post-error compensation algorithm using convolutional and long short-term memory neural networks (CNN-LSTMs). By leveraging convolution feature extraction capabilities and considering the temporal dependencies of dynamic measurement parameters with LSTM, the model demonstrates a stronger ability to learn from severely coupled time series data, resulting in a significant improvement in the compensation performance. …”
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Scheme Analysis for Enhancing Autonomous Driving Based on Computer Vision by Chen Xinyi, Luo Binbin, Xu Ziyue

“…Thesis adopted both literature review and technical analysis, focusing on recent developments in key technologies such as image processing, hybrid convolutional neural network (CNN)-transformer models, object detection, and multi-sensor fusion. …”
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HSSCIoT: An Optimal Framework Based on Internet of Things-Cloud Computing for Healthcare Services Selection in Smart Hospitals by Oanh Nguyen

“…The combination of Recurrent Neural Networks (RNN) means Long Term Short Memory (LSTM) and new kinds of Convolutional Neural Networks (CNN) means Atrous Spatial Pyramid Pooling (ASPP) deep learning methods considered for HSSCIoT.…”
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Query-Based Instance Segmentation with Dual Attention Transformer for Autonomous Vehicles by Aya Taourirte, Li-Hong Juang

“…First, we replace the traditional CNN backbone with the DaViT Transformer to extract richer, multi-scale features. …”
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Application of Rotating Machinery Fault Diagnosis Based on Deep Learning by Wei Cui, Guoying Meng, Aiming Wang, Xinge Zhang, Jun Ding

“…After a brief review of early fault diagnosis methods, this paper focuses on the method models that are widely used in deep learning: deep belief networks (DBN), autoencoders (AE), convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), and transfer learning methods are summarized from the two aspects of principle and application in the field of fault diagnosis of rotating machinery. …”
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An extensive bibliometric analysis of pavement deterioration detection using sensors and machine learning: Trends, innovations, and future directions by Mehmet Rizelioğlu

“…In addition, accelerometer and GPS were the most used sensors, ANN and CNN were the most preferred algorithms, and cracks and potholes were the most studied topics. …”
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An Orderly EV Charging Scheduling Method Based on Deep Learning in Cloud-Edge Collaborative Environment by Jiayong Zhong, Xiaofu Xiong

“…Then, the load demands and renewable outputs are predicted by a model combined with the convolutional neural network and deep belief network (CNN-DBN). Secondly, the power supply plans for charging stations are determined at the cloud side aiming at minimizing the operating cost of the distribution network via collecting the forecasting results. …”
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Urban Traffic Flow Forecast Based on FastGCRNN by Ya Zhang, Mingming Lu, Haifeng Li

“…The existing works adopted RNN and CNN/GCN, among which GCRN is the state-of-the-art work, to characterize the temporal and spatial correlation of traffic flows. …”
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Detection of Data Integrity Attack Using Model and Data-Driven-Based Approach in CPPS by G. Y. Sree Varshini, S. Latha

“…The convolutional neural network- (CNN-) based data-driven anomaly detection technique outperforms other machine learning (ML) techniques such as support vector machine (SVM), K-nearest neighbour (KNN), and random forest (RF). …”
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Optimising deep learning models for ophthalmological disorder classification by S. Vidivelli, P. Padmakumari, C. Parthiban, A. DharunBalaji, R. Manikandan, Amir H. Gandomi

“…In this article, we have used different deep learning models for the categorisation of ophthalmological disorders into multiple classes and multiple labels utilising transfer learning-based convolutional neural network (CNN) methods. The Ocular Disease Intelligent Recognition (ODIR) database is used for experiments, and it contains fundus images of the patient’s left and right eyes. …”
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MASFNet: Multi-level attention and spatial sampling fusion network for pine wilt disease trees detection by Dong Ren, Meng Li, Ziyu Hong, Li Liu, Jingfeng Huang, Hang Sun, Shun Ren, Pan Sao, Wenbin Wang, Jingcheng Zhang

“…Experiments were conducted using seven different models (Faster R-CNN, Cascade R-CNN, CenterNet, FCOS, YOLOX, YOLOv7, and MASFNet), with mean average precision (mAP50) ranging from 0.717 to 0.885. …”
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Adaptive spatial-channel feature fusion and self-calibrated convolution for early maize seedlings counting in UAV images by Zhenyuan Sun, Zhenyuan Sun, Zhenyuan Sun, Zhi Yang, Zhi Yang, Yimin Ding, Yimin Ding, Boyan Sun, Boyan Sun, Saiju Li, Saiju Li, Zhen Guo, Zhen Guo, Lei Zhu, Lei Zhu

“…By integrating RSCconv and ASCFF into other detection frameworks such as Faster R-CNN, RetinaNet, and Deformable DETR, we observed enhanced detection and counting accuracy, further validating the effectiveness of our proposed method. …”
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A Vortex Identification Method Based on Extreme Learning Machine by Jun Wang, Lei Guo, Yueqing Wang, Liang Deng, Fang Wang, Tong Li

“…Aiming at the above problems, we present a novel vortex identification method based on the Convolutional Neural Networks-Extreme Learning Machine (CNN-ELM). This method transforms the vortex identification problem into a binary classification problem, and can quickly, objectively, and robustly identify vortices from the flow field. …”
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Predicted Anchor Region Proposal with Balanced Feature Pyramid for License Plate Detection in Traffic Scene Images by Hoanh Nguyen

“…Recently, many deep learning-based networks have been proposed and achieved incredible success in general object detection, such as faster R-CNN, SSD, and R-FCN. However, directly applying these deep general object detection networks on license plate detection without modifying may not achieve good enough performance. …”
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Brain-inspired multimodal motion and fine-grained action recognition by Yuening Li, Xiuhua Yang, Changkui Chen

“…These methods struggle particularly with video data containing complex combinations of actions and subtle motion variations.MethodsTypically, they depend on handcrafted feature extractors or simple convolutional neural network (CNN) architectures, which makes effective multimodal fusion challenging. …”
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