An Operating Status Analysis System of Reactor Equipment Based on Voiceprint Recognition Technology by Litan Cao, Huabing Wei, Zhi Huang, Minglei Shi

“…The recognition pattern based on a deep learning convolutional neural network was established. Through experiments, it was found that aiming at the additive superposition problem of transformer sound generated by a stable working condition and unstable instantaneous noise, a new method based on the cosine similarity algorithm was proposed to realize the separation detection of sound pattern superposition. …”
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Foreign object detection on coal conveyor belt enhanced by attention mechanism by ZHANG Yang, CHENG Zhiyu, CHEN Yunjiang, ZHANG Jiannan, YUAN Wensheng, ZHANG Hui

“…In order to solve this problem, the network structure of the original YOLOv8 algorithm was optimized and a YOLOv8-CPCA detection method was proposed. …”
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Vehicle Traffic Estimation Using Deep Learning by Meetkumar Patel, Daniel Silver

“…Thus, we design and develop a machine learning approach which can predict vehicular traffic density and flowrate up to two days in the future based on the weather, calendar and special events data. First, Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) networks are utilized to predict the number of new vehicles and the total number of vehicles in images captured by a Nova Scotia Webcams (NS Webcams) video camera. …”
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Corrosion resistance prediction of high-entropy alloys: framework and knowledge graph-driven method integrating composition, processing, and crystal structure by Guangxuan Song, Dongmei Fu, Yongjie Lin, Lingwei Ma, Dawei Zhang

“…This study proposes the Composition and Processing-Driven Two-Stage Corrosion Prediction Framework with Structural Prediction (CPSP Framework), which first predicts crystal structure and then combines composition and processing data for corrosion current prediction. A deep learning model, Mat-NRKG, is developed based on the CPSP framework, efficiently integrating composition, processing, and crystal structure data through a knowledge graph and graph convolutional network. …”
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UMEDNet: a multimodal approach for emotion detection in the Urdu language by Adil Majeed, Hasan Mujtaba

“…UMEDNet leverages state-of-the-art techniques for feature extraction across modalities; for extracting facial features from video, both Multi-task Cascaded Convolutional Networks (MTCNN) and FaceNet were used with fine-tuned Wav2Vec2 for speech features and XLM-Roberta for text. …”
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FruitNet: Lightweight CNN for High-Throughput Image-Based Fruit Yield Estimation by Yadav Kamlesh Kumar, Tandan Gajendra

“…Innovations in this work include the most lightweight Convolutional Neural Network (CNN) named FruitNet proposed for achieving high throughput and image based estimation offrait yield. …”
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A data Mining Approach using CNN and LSTM to Predict Divorce before Marriage by Touba Torabipour, Safieh Siadat, Hosein Taghavi

“…The primary data of this study was collected from the information of 35,000 couples registered in the National Organization for Civil Registration of Iran during 2018-2019. In the current work, we proposed a method to predict divorce by combining a convolutional neural network (CNN) and long short-term memory (LSTM). …”
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Open-Circuit Fault Diagnosis Method of Energy Storage Converter Based on MFCC Feature Set by Bin YU, Xingrong SONG, Ting ZHOU, Linbo LUO, Hui LI, Liang CHE

“…Secondly, a fault state diagnosis model based on the Bayesian optimization algorithm (BOA) and one-dimensional convolutional neural network (CNN-1D) is constructed with a low-dimensional fault feature set as an input. …”
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Research on Multi-Objective Pedestrian Tracking Algorithm Based on Full-Size Feature Fusion by Yihuai Zhu, Zhandong Liu, Ke Li, Yong Li, Xiangwei Qi, Nan Ding

“…Most existing pedestrian re-identification methods are based on convolutional neural networks (CNNs), which struggle to balance both local and global features of pedestrians. …”
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An approach for predicting landslide susceptibility and evaluating predisposing factors by Wanxin Guo, Jian Ye, Chengbing Liu, Yijie Lv, Qiuyu Zeng, Xin Huang

“…Using a stacking method, a 1D convolutional neural network (1D-CNN), a recurrent neural network (RNN), and a long short-term memory (LSTM) network were combined to form the CRNN-LSTM ensemble model. …”
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Research and Experiment on a Chickweed Identification Model Based on Improved YOLOv5s by Hong Yu, Jie Zhao, Xiaobo Xi, Yongbo Li, Ying Zhao

“…Currently, multi-layer deep convolutional networks are mostly used for field weed recognition to extract and identify target features. …”
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Deep Forest Modeling: An Interpretable Deep Learning Method for Mineral Prospectivity Mapping by Yue‐Lin Dong, Zhen‐Jie Zhang

“…Predictions are made using Deep Forest with factors like Euclidean distance between faults and magmatic rock, fault line density, gravity anomalies, and stream‐sediment geochemical data. Deep neural networks, random forest, convolutional neural networks, transformer model and graph convolutional networks are also used for comparison. …”
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A Computational Model of Attention-Guided Visual Learning in a High-Performance Computing Software System by Alice Ahmed, Md. Tanim Hossain

“…Transformer blocks use parallelism and less localized attention than current or convolutional models. The study investigates the use of transformer topologies to enhance language modeling, focusing on attention-guided learning and attention-modulated Hebbian plasticity. …”
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Diagnosis of Alzheimer’s disease using brain $$^{18}\textrm{F}$$ -FDG PET imaging based on a state space model by Yufang Dong, Yonglin Chen, Zhe Jin, Xingbo Dong

“…Abstract In recent studies on Alzheimer’s disease (AD), various network models have shown significant potential in disease prediction. …”
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Screen shooting resistant watermarking based on cross attention by Lianshan Liu, Peng Xu, Qianwen Xue

“…Most existing solutions are based on Convolutional Neural Networks (CNNs) for the embedding of watermarks. …”
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The applications of CT with artificial intelligence in the prognostic model of idiopathic pulmonary fibrosis by Zeyu Chen, Zheng Lin, Zihan Lin, Qi Zhang, Haoyun Zhang, Haiwen Li, Qing Chang, Jianqi Sun, Feng Li

“…Artificial intelligence (AI) algorithms, including principal component analysis, support vector machine, random survival forest, and convolutional neural network, could be applied to the procedure of IPF prognostic model, that is, region of interest extraction, image feature selection, clinical feature selection, and model construction. …”
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A high precision YOLO model for surface defect detection based on PyConv and CISBA by Shufen Ruan, Chenmei Zhan, Bo Liu, Quan Wan, Kunfang Song

“…The algorithm first introduces multi-scale attention modules and uses two newly designed pyramid convolutions in the backbone network to better identify multi-scale defects; Secondly, Soft-NMS is introduced to replace traditional NMS, which can reduce information loss and improve multi-target detection accuracy by smoothing and suppressing the scores of overlapping boxes. …”
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Development and Practice of Cloud Collaborative Platform for Downhole Measurement Tools by Che Yang, Yuan Guangjie, Qian Hongyu, Du Weiqiang, Wang Chenlong, Ding Jiping

“…Moreover， to solve the problem of low far-field ranging accuracy， multiple magnetic steering data mining algorithms such as support vector machine （SVM）， decision tree （DT）， multilayer perceptron （MLP） and convolutional neural network （CNN） were built and compared， indicating that the robustness and generalization of the multilayer perceptron algorithm is the best. …”
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AI-Driven Detection of Obstructive Sleep Apnea Using Dual-Branch CNN and Machine Learning Models by Manjur Kolhar, Manahil Muhammad Alfridan, Rayan A. Siraj

“…<b>Background/Objectives:</b> The purpose of this research is to compare and contrast the application of machine learning and deep learning methodologies such as a dual-branch convolutional neural network (CNN) model for detecting obstructive sleep apnea (OSA) from electrocardiogram (ECG) data. …”
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A Lightweight Load Identification Model Update Method Based on Channel Attention by Yong Gao, Junwei Zhang, Mian Wang, Zhukui Tan, Minhang Liang

“…To overcome this challenge, we construct color V-I trajectory maps by extracting the voltage and current signals of electrical devices during steady-state operation, and combine the convolutional neural network and channel attention mechanism for feature extraction and classification. …”
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