Fluorescence Spectroscopy and a Convolutional Neural Network for High-Accuracy Japanese Green Tea Origin Identification by Rikuto Akiyama, Kana Suzuki, Yvan Llave, Takashi Matsumoto

“…The fluorescence of each sample was measured in the range of 250 to 550 nm, highlighting the differences in chemical composition that reflect each region. …”
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A rapid fingerprint positioning method based on deep convolutional neural network for MIMO-OFDM systems by Chenlin He, Xiaojun Wang, Jiyu Jiao, Yuhua Huang, Chengpei Han, Yizhuo Zhang, Jianping Zhu

“…The cosine distance is chosen as the fingerprint similarity criterion due to its sensitivity to angular differences. First, the DCNN model is used to determine the sub-area to which the mobile terminal belongs, and then the weighted K-nearest neighbor (WKNN) matching algorithm is used to estimate the position within the sub-area. …”
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Classification of Short-Segment Pediatric Heart Sounds Based on a Transformer-Based Convolutional Neural Network by Md Hassanuzzaman, Samit Kumar Ghosh, Mohammad Nurul Akhtar Hasan, Mohammad Abdullah Al Mamun, Khawza I. Ahmed, Raqibul Mostafa, Ahsan H. Khandoker

“…This study investigates the minimum signal duration required for accurate automatic classification of heart sounds and evaluates signal quality using the root mean square of successive differences (RMSSD) and the zero-crossing rate (ZCR). …”
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Robust Registration of Multimodal Remote Sensing Images Using Self-Similar Adjacent Self-Convolutional Feature by Tian Gao, Chaozhen Lan, Liang Lv, Qunshan Shi, Wenjun Huang, Yiqiao Wang, Zhizhen Mu

“…Due to significant nonlinear radiometric differences (NRD) and severe geometric distortions (e.g., translation, scale, and rotation) among multimodal images (e.g., optical, infrared, SAR), image registration remains a challenging problem. …”
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Cross-User Electromyography Pattern Recognition Based on a Novel Spatial-Temporal Graph Convolutional Network by Mengjuan Xu, Xiang Chen, Yuwen Ruan, Xu Zhang

“…The research results of this paper indicate that GNN has certain advantages in overcoming the impact of individual differences, and can be used to provide possible solutions for achieving robust EMG pattern recognition technology.…”
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Comparative Analysis of Machine Learning Based Approaches for Face Detection and Recognition by Ratnesh Kumar Shukla, Arvind Kumar Tiwari

“…We also show that deeper discrimination can be learned by creating a deep network that retains inter-cluster differences both and within groups. MobileNet, which provides both offline and real-time precision and speed to provide fast and consistent stable results, is the recently suggested Convolutional Neural network (CNN) model. …”
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Convolution Results and Fekete–Szegö Inequalities for Certain Classes of Symmetric q-Starlike and Symmetric q-Convex Functions by Tamer M. Seoudy

“…In this paper, by using the concept of the symmetric q-difference operator, we introduce certain classes of symmetric q-starlike and symmetric q-convex functions. …”
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A Seq-to-Seq Temporal Convolutional Network for Volleyball Jump Monitoring Using a Waist-Mounted IMU by Meng Shang, Camilla de Bleecker, Jos Vanrenterghem, Roel de Ridder, Sabine Verschueren, Carolina Varon, Walter de Raedt, Bart Vanrumste

“…Also, existing methods do not provide accurate differentiation between different types of jumps. In this study, an unobtrusive system with a single inertial measurement unit (IMU) on the waist was proposed to recognize the types of volleyball jumps. …”
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Characteristics Analysis of Mental Health Data of College Students Based on Convolutional Neural Network and TOPSIS Evaluation Model by Lanfeng Zhou

“…This paper develops a feature analysis method of the mental health data of students in different colleges and regions and of different ages based on a convolutional neural network and TOPSIS evaluation model and studies the college students’ mental health analysis model based on convolutional neural network. …”
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Robust Low-Snapshot DOA Estimation for Sparse Arrays via a Hybrid Convolutional Graph Neural Network by Hongliang Zhu, Hongxi Zhao, Chunshan Bao, Yiran Shi, Wenchao He

“…The C-GNN architecture combines 1D convolutional layers for local spatial feature extraction with graph convolutional layers for global structural learning, effectively capturing both fine-grained and long-range array dependencies. …”
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Cross-species AI: shifting a convolutional neural network from pigs to lambs to detect pneumonia at slaughter by Anastasia Romano, Antonio De Camillis, Domenico Sciota, Simona Baghini, Andrea Di Provvido, Alfonso Rosamilia, Andrea Capobianco Dondona, Nicola Bernabò, Francesca Vaccarelli, Attilio Corradi, Giuseppe Marruchella

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Using convolutional neural networks to count parrot nest‐entrances on photographs from the largest known colony of Psittaciformes by Gabriel L. Zanellato, Gabriel A. Pagnossin, Mauricio Failla, Juan F. Masello

“…Compared to a previous study conducted at El Cóndor colony more than 20 years ago, the CNN architectures also detected noteworthy differences in the distribution of the nest‐entrances along the cliff. …”
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MDGCN: Multiple Graph Convolutional Network Based on the Differential Calculation for Passenger Flow Forecasting in Urban Rail Transit by Chenxi Wang, Huizhen Zhang, Shuilin Yao, Wenlong Yu, Ming Ye

“…However, the spatiotemporal relationship between stations not only is limited to geospatial adjacency, but also lacks different perspectives of station correlation analysis. …”
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Enhanced Community Detection via Convolutional Neural Network: A Modified Approach Based on MRFasGCN Algorithm by Puneet Kumar, Dalwinder Singh, Mamoona Humayun, Ali Alqazzaz, Arun Malik, Ibrahim Alrashdi, Isha Batra, Ghadah Naif Alwakid

“…Initially, Newman and Girvan proposed traditional algorithms for community detection from social networks in 2004, but with the growth of social networks, Convolutional Neural Network (CNN) based algorithms are proposed by different researchers in recent years due to the inefficiency of traditional methods. …”
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Hybrid Convolutional Neural Network-Transformer Model for End-to-End Binaural Sound Source Localization in Reverberant Environments by Xinyi Chen, Lijia Zhao, Jie Cui, Hua Li, Xiaodong Wang

“…The WavLocT model has a unique feature extraction block that incorporates a convolutional neural network (CNN) and transformer structure, in which binaural localization features are extracted from different subband signal waveforms via the CNN structure. …”
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Gradual poisoning of a chest x-ray convolutional neural network with an adversarial attack and AI explainability methods by Stephen B. Lee

“…The study uses various poisoned datasets of different compositions (2%, 16.7% and 100% ratios of poisoned data) and two different test sets (a normal set of test data and one that contained poisoned images) to study the effects of BadNets. …”
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A Deep Learning Inversion Method for Airborne Time-Domain Electromagnetic Data Using Convolutional Neural Network by Xiaodong Yu, Peng Zhang, Xi Yu

“…In this paper, we present a deep learning inversion method that can be used to solve the fast inversion problem of airborne time-domain electromagnetic data; the method uses a one-dimensional convolutional neural network. The network structure consists of two parts containing different numbers of convolutional and pooling layers. …”
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A new framework for mental illnesses diagnosis using wearable devices aided by improved convolutional neural network by Hend S. Saad, John F. W. Zaki, Mohamed M. Abdelsalam

“…MHD diseases are difficult to be distinguished from each other because they come in many forms with different severity of symptoms and different periods of suffering. …”
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Intelligent Fault Diagnosis of Hydraulic System Based on Multiscale One-Dimensional Convolutional Neural Networks with Multiattention Mechanism by Jiacheng Sun, Hua Ding, Ning Li, Xiaochun Sun, Xiaoxin Dong

“…Subsequently, to effectively utilize the feature information of different frequencies, the HAM is integrated into the 1DCNN to form the MA-MS1DCNN. …”
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Vibration Signal-Based Fault Diagnosis of Rotary Machinery Through Convolutional Neural Network and Transfer Learning Method by Chirag Mongia, Shankar Sehgal

“…To address this limitation, a transfer learning (TL)-based methodology has been developed for bearing fault detection, so that the model trained under some specific training conditions can perform accurately under significantly different real-time working conditions, thereby significantly improving diagnostic efficiency while reducing training time. …”
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