HybNet: A hybrid deep models for medicinal plant species identification by B.R. Pushpa, S. Jyothsna, S. Lasya

“…Images captured under unconstrained environments, scale variations, different lighting conditions, leaf orientation, complicated backdrops, and leaflet structure make plant species recognition rigorous and time-consuming. …”
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Mechanical equipment fault diagnosis method based on improved deep residual shrinkage network. by Shaoming Qiu, Liangyu Liu, Yan Wang, Xinchen Huang, Bicong E, Jingfeng Ye

“…In pursuit of network optimization and parameter reduction, we have strategically incorporated depthwise separable convolutions, effectively replacing conventional convolutional layers. …”
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A Generalized Zero-Shot Deep Learning Classifier for Emotion Recognition Using Facial Expression Images by Vishal Singh Bhati, Namita Tiwari, Meenu Chawla

“…This study proposes a generalized deep learning classifier, Generalized Zero-Shot Convolutional Neural Network (GZS-ConvNet), to solve the problem of generalization. …”
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Dynamic Scene Segmentation and Sentiment Analysis for Danmaku by Limin Li, Jie Jing, Peng Shi

“…With this as a base, a new Danmaku-E model is made to find and group seven different emotional categories within Danmaku comments. …”
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Identification of Alzheimer’s disease brain networks based on EEG phase synchronization by Jiayi Cao, Bin Li, Xiaoou Li

“…Abstract Objective Using the phase synchronization of EEG signals, two different phases, PLI and PLV, were used to construct brain network analysis and graph convolutional neural network, respectively, to achieve automatic identification of Alzheimer’s disease (AD) and to assist in the early diagnosis of Alzheimer’s disease. …”
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Assessment of a Hyperspectral Remote Sensing Model Performance for Particulate Phosphorus in Optically Shallow Lake Water by Banglong Pan, Wuyiming Liu, Zhuo Diao, Qianfeng Gao, Lanlan Huang, Shaoru Feng, Juan Du, Qi Wang, Jiayi Li, Jiamei Cheng

“…The applicability of backpropagation (BP) neural network, random forest (RF), convolutional neural network (CNN), and CNN-RF models for remote sensing inversion of PP concentration is assessed through model comparison. …”
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Differentiating localized autoimmune pancreatitis and pancreatic ductal adenocarcinoma using endoscopic ultrasound images with deep learning by Hitomi Nakamura, Motohisa Fukuda, Akiko Matsuda, Naohiko Makino, Hirohito Kimura, Yu Ohtaki, Yoshihito Nawa, Soushi Oyama, Yuya Suzuki, Toshikazu Kobayashi, Tetsuya Ishizawa, Yasuharu Kakizaki, Yoshiyuki Ueno

“…We applied transfer learning to a convolutional neural network called ResNet152, together with our innovative imaging method contributing to data augmentation and temporal data process. …”
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Development and evaluation of deep neural networks for the classification of subtypes of renal cell carcinoma from kidney histopathology images by Amit Kumar Chanchal, Shyam Lal, Shilpa Suresh

“…Further, to improve the network’s representation power, a CNN module called Group Convolutional Deep Localization (GCDL) has been introduced, which effectively integrates three different feature descriptors. …”
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Oriented R-CNN With Disentangled Representations for Product Packaging Detection by Jiangyi Pan, Jianjun Yang, Yinhao Liu, Yijie Lv

“…Furthermore, targets in varying backgrounds necessitate different receptive fields, which can be dynamically adjusted using different convolutional kernels. …”
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Using a Solution Construction Algorithm for Cyclic Shift Network Coding under Multicast Network to the Transformation of Musical Performance Styles by Xiuqin Wang, Jun Geng, Zhiyuan Li

“…A two-way recurrent neural network based on the gated recurrent unit is used to extract a sequence of note feature vectors of different styles, and a one-dimensional convolutional neural network is used to predict the intensity of the extracted note feature vector sequence for a specific style, which better learns the intensity variation of different styles of MIDI music.…”
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AI-based orchard monitoring at night: Enhancing sustainable fruit production through real-time apple detection by Kutyrev Alexey, Khort Dmitry, Smirnov Igor, Zubina Valeria

“…Accurate recognition, classification and segmentation of apple fruits on tree crowns are of key importance for improving the efficiency of remote monitoring and forecasting of fruit orchard yields at different stages of the production process. The study evaluates the performance of the state-of-the-art convolutional neural network model YOLO11 (You Only Look Once version 11) under artificial lighting conditions at night. …”
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Prediction of COVID-19 cases by multifactor driven long short-term memory (LSTM) model by Yanwen Shao, Tsz Kin Wan, Kei Hang Katie Chan

“…Through the selection of several important features, we identified the factors that have stronger impact on the increase of new cases in different groups. Then, we use a long-time span data to predict the future COVID-19 new cases by training a long short-term memory (LSTM) model, a support vector regressor (SVR) and a temporal convolutional network (TCN), among which LSTM possessed the best performance and offered a good generalization ability. …”
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Image retrieval method based on data mining and deep residual network by Hongzhi Yuan, Wei Hu

“…The experimental results showed that the improved model achieved an average retrieval accuracy of 81.1 % on three different image sets, which was 27 percentage points higher than that of the traditional model. …”
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Pilot Maneuvering Performance Analysis and Evaluation with Deep Learning by Shiwen Zhang, Zhimei Huo, Yanjin Sun, Fujuan Li, Bo Jia

“…Finally, the indicators were grouped into 5 common factors by factor analysis and fed into 1-D CNN in different combinations. Each common factor plays a different role in pilot performance evaluation, which can provide advice for the future.…”
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Real-time Jordanian license plate recognition using deep learning by Salah Alghyaline

“…Countries have different specifications for License Plates (LPs), therefore developing one Automatic license plate recognition (ALPR) system that works well for all LPs types is a difficult task. …”
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Federated Learning and EEL-Levy Optimization in CPS ShieldNet Fusion: A New Paradigm for Cyber–Physical Security by Nalini Manogaran, Yamini Bhavani Shankar, Malarvizhi Nandagopal, Hui-Kai Su, Wen-Kai Kuo, Sanmugasundaram Ravichandran, Koteeswaran Seerangan

“…This involves the incorporation of the Federated Residual Convolutional Network into an optimization method called EEL-Levy Fusion Optimization. …”
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An Efficient Encoding Spectral Information in Hyperspectral Images for Transfer Learning of Mask R-CNN for Instance Segmentation of Tomato Sepals by Zeljana Grbovic, Marko Panic, Vladan Filipovic, Sanja Brdar, Hendrik de Villiers, Manon Mensink, Aneesh Chauhan

“…Additionally, we incorporated a custom convolutional layer initialized it with estimated NMF coefficients, and achieved a mAP score of 87.40%. …”
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CIA-UNet: An Attention-Enhanced Multi-Scale U-Net for Single Tree Crown Segmentation by Jiapeng Duan, Chuanzhao Tian, Wansi Liu, Lixiang Cao, Teng Feng, Xiaomin Tian

“…In this model, the multiscale feature extraction capability of the InceptionV2 module is leveraged to compensate for U-Net’s limitation in capturing different scales of tree crowns and fine details using fixed convolutional kernels. …”
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Intelligent measurement of adolescent idiopathic scoliosis x-ray coronal imaging parameters based on VB-Net neural network: a retrospective analysis of 2092 cases by Jinlong Liu, Haoran Zhang, Pei Dong, Danyang Su, Zhen Bai, Yuanbo Ma, Qiuju Miao, Shenyu Yang, Shuaikun Wang, Xiaopeng Yang

“…This study is based on the accurate and rapid measurement of x-ray coronal imaging parameters in AIS patients by AI, to explore the differences and correlations, and to further investigate the risk factors in different groups, so as to provide a theoretical basis for the diagnosis and surgical treatment of AIS. …”
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Enhancing corn industry sustainability through deep learning hybrid models for price volatility forecasting. by Chengjin Yang, Yanzhong Zhai, Zehua Liu

“…The model integrates a three-layer decomposition combined dual-filter time-series denoising method (TLDCF-TSD), a bidirectional time-convolutional enhancement network (BiTCEN), a bidirectional long- and short-term memory network (BiLSTM), and a frequency-enhanced channel attention mechanism (FECAM) to improve prediction accuracy and robustness. …”
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