RiceNet: Efficient CNN for High-Throughput Image-Based Rice Panicle Detection and Counting by Kushwaha Ragini, Balkrishna Sutar Manisha

“…RiceNet achieves high accuracy and computational efficiency over traditional image processing and other CNN architectures on diverse rice field images of different rice varieties and stages of growth. Notably, the model can yield timely estimates of crop yield and manages the crop within 30 seconds, which is a significant reduction in panicle detection time. …”
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A spatial interpolation method based on 3D-CNN for soil petroleum hydrocarbon pollution. by Sheng Miao, Guoqing Ni, Guangze Kong, Xiuhe Yuan, Chao Liu, Xiang Shen, Weijun Gao

“…By introducing Channel Attention Mechanism (CAM), the model assigns different weights to auxiliary variables, improving the prediction accuracy of soil hydrocarbon content. …”
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Robust Formation Control for Unmanned Ground Vehicles Using Onboard Visual Sensors and Machine Learning by Mingfei Li, Haibin Liu, Feng Xie

“…Simulation results show that the control strategy combining TSTMIPI and BSE not only eliminates the reliance on external markers but also significantly improves control precision under different noise levels and visual occlusion conditions, surpassing existing visual formation control methods in maintaining the desired distance and angular precision.…”
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Author name disambiguation based on heterogeneous graph neural network. by Ge Wang, Zikai Sun, Weiyang Hu, MengHuan Cai

“…As the existing graph heterogeneous neural network can not learn different types of nodes and edge interaction, add multiple attention, design ablation experiments to verify its impact on the network. …”
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COVID-19 Tweets Classification during Lockdown Period Using Machine Learning Classifiers by Syed Ali Jafar Zaidi, Indranath Chatterjee, Samir Brahim Belhaouari

“…As a result, social media platforms have always had a difficult time authenticating this fake information. Different machine learning (ML) and deep learning (DL) classifiers were used in this work to categorize the continuing impacts of tweets and forecast their after-effects. …”
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An Augmented AutoEncoder With Multi-Head Attention for Tool Wear Prediction in Smart Manufacturing by Chunping Dong, Jiaqiang Zhao

“…The decoder includes Multi-Head Attention (MHA) and Gated Recurrent Unit (GRU), which can adaptively enhance the relevant feature weights and extract long-term, deep different features. For the model training, a monotonicity loss function is defined. …”
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Autonomous Maneuver Decision of UCAV Air Combat Based on Double Deep Q Network Algorithm and Stochastic Game Theory by Yuan Cao, Ying-Xin Kou, Zhan-Wu Li, An Xu

“…Air combat simulation results show that UCAV can choose maneuvers autonomously under different situations and occupy a dominant position quickly by this method, which greatly improves the combat effectiveness of UCAV.…”
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Wavelet attention-based implicit multi-granularity super-resolution network by Chen Boying, Shi Jie

“…Compared to existing self-attention modules, the wavelet attention module decomposes image features into different frequency components using wavelet transforms. …”
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Research on road surface damage detection based on SEA-YOLO v8. by Yuxi Zhao, Baoyong Shi, Xiaoguang Duan, Wenxing Zhu, Liying Ren, Chang Liao

“…Firstly, the SBS module is constructed to optimize the computational complexity, achieve real-time target detection under limited hardware resources, successfully reduce the model parameters, and make the model more lightweight; Secondly, we integrate the EMA attention mechanism module into the neck component, enabling the model to utilize feature information from different layers, enabling the model to selectively focus on key areas and improve feature representation; Then, an adaptive attention feature pyramid structure is proposed to enhance the feature fusion capability of the network; Finally, lightweight shared convolutional detection head (LSCD-Head) is introduced to improve feature representation and reduce the number of parameters. …”
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A Reconfigurable Coarse-to-Fine Approach for the Execution of CNN Inference Models in Low-Power Edge Devices by Auangkun Rangsikunpum, Sam Amiri, Luciano Ost

“…To efficiently utilise different fine models on low-cost FPGAs with area minimisation, ZyCAP-based PR is adopted. …”
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Mitigating data bias and ensuring reliable evaluation of AI models with shortcut hull learning by Wenhao Zhou, Faqiang Liu, Hao Zheng, Rong Zhao

“…Here, we introduce shortcut hull learning, a diagnostic paradigm that unifies shortcut representations in probability space and utilizes diverse models with different inductive biases to efficiently learn and identify shortcuts. …”
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Artificial Intelligence for Land Cover and Land Use Classification in Remote Sensing: Review Study by R. AlAli

“…This paper presents a comparative study of the different methods used in Land Cover Land Use Classification to find out the best available method based on their accuracy.…”
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Advancements in handwritten Devanagari character recognition: a study on transfer learning and VGG16 algorithm by Chetan Sharma, Shamneesh Sharma, Sakshi, Hsin-Yuan Chen

“…For future research, the authors intend to investigate deeper learning structures further and integrate a broader and more varied dataset to enhance the model’s accuracy and guarantee its suitability for different real-life situations.…”
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Predictive Analysis of Maritime Congestion Using Dynamic Big Data and Multiscale Feature Analysis by Yalin Wu

“…Second, the multiscale feature analysis provides a comprehensive understanding of maritime network congestion by examining it from different perspectives and scales, leading to more accurate predictions and effective congestion management strategies. …”
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Edge intelligence for poultry welfare: Utilizing tiny machine learning neural network processors for vocalization analysis. by Ramasamy Srinivasagan, Mohammed Shawky El Sayed, Mohammed Ibrahim Al-Rasheed, Ali Saeed Alzahrani

“…The study emphasizes accurately identifying and categorizing different chicken noises associated with emotional states such as discomfort, hunger, and satisfaction. …”
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A Deep Learning Framework for Damage Assessment of Composite Sandwich Structures by Viviana Meruane, Diego Aichele, Rafael Ruiz, Enrique López Droguett

“…The proposed methodology is validated using numerical and experimental data from a composite sandwich panel with different damage configurations.…”
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Classification of Ship Type from Combination of HMM–DNN–CNN Models Based on Ship Trajectory Features by Dae-Woon Shin, Chan-Su Yang

“…This study proposes an enhanced ship-type classification model that employs a sequential processing methodology integrating hidden Markov model (HMM), deep neural network (DNN), and convolutional neural network (CNN) techniques. Four different ship types—fishing boat, passenger, container, and other ship—were classified using multiple ship trajectory features extracted from the automatic identification system (AIS) and small fishing vessel tracking system. …”
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Diagnosis of depression based on facial multimodal data by Nani Jin, Renjia Ye, Peng Li

“…Through the multi-modal feature fusion, the model can effectively capture different feature patterns related to depression.ResultsWe conduct extensive experiments on the publicly available clinical dataset, the Extended Distress Analysis Interview Corpus (E-DAIC). …”
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Irrigated rice-field mapping in Brazil using phenological stage information and optical and microwave remote sensing by Andre Dalla Bernardina Garcia, MD Samiul Islam, Victor Hugo Rohden Prudente, Ieda Del’Arco Sanches, Irene Cheng

“…We divide the growth cycle into different rice phenological stages: beginning, middle and end of season, as well as the season transition periods. …”
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Enhanced Rolling Bearing Fault Diagnosis Using Multimodal Deep Learning and Singular Spectrum Analysis by Yunhang Wang, Hongwei Wang, Ruoyang Bai, Yuxin Shi, Xicong Chen, Qingang Xu

“…Based on this, a recursive gated convolutional neural network (RGCNN) is designed to process the STFT image data, while a 1D convolutional neural network (1DCNN) is specifically optimized for training with time series data. …”
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