Attention mechanism based CNN-LSTM hybrid deep learning model for atmospheric ozone concentration prediction by Jiang Yuan, Hua Dengxin, Wang Yufeng, Yang Xueting, Di Huige, Yan Qing

“…It also exhibits consistent accuracy across different seasons, highlighting its robustness and superior time-series prediction capabilities for ozone concentrations.…”
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Devanagari Character Recognition: A Comprehensive Literature Review by Sandhya Arora, Latesh Malik, Sonakshi Goyal, Debotosh Bhattacharjee, Mita Nasipuri, Ondrej Krejcar

“…The Devanagari script originated from the ancient Brahmi script and is a widely used Indic script for writing different languages, like Sanskrit, Hindi, Marathi, Nepali, and Konkani. …”
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The Short‐Time Prediction of the Energetic Electron Flux in the Planetary Radiation Belt Based on Stacking Ensemble‐Learning Algorithm by Rongxin Tang, Yuhao Tao, Jiahao Li, Zhou Chen, Xiaohua Deng, Haimeng Li

“…In order to predict the variations of energetic electron fluxes for different energy channels, we proposed a new ensemble machine leaning model for differential electron flux from 30 keV to 4 MeV in the Earth's radiation belts based on the RBSP‐A observation data from March 2013 to December 2017. …”
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Deep learning-based object detection and robotic arm grasping by ZHANG Lei, ZHANG Senhui, YAN Song, YUAN Yuan

“…Secondly, to enhance the feature extraction capabilities of the grasping network, the parallel use of different-size convolutional kernels in the Inception-ResNet module was utilized to broaden the network's receptive field. …”
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Efficient Real-Time Pathfinding for Visually Impaired Individuals by Tadeh Ghahremanians, Hossein Mahvash Mohammadi

“…In contrast, deep learning models such as instance segmentation and semantic segmentation allow for independent recognition of different elements within a scene. In this research, deep convolutional neural networks are employed to perform semantic segmentation of camera images, thereby facilitating the identification of patterns across the image’s feature space. …”
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Classification of first embryonic division stages of multiple Caenorhabditis species by deep learning by Dhruv Khatri, Prachi Negi, Chaitanya A. Athale

“…Three previously described networks, ResNet, VggNet, and EfficientNet, and a customized shallow network, which we refer to as EvoCellNet, achieved 91% or greater accuracy in test data from 23 different nematode species. We find activation vectors of the CNNs of the sparse EvoCellNet correlate with spatial localization of intracellular features of multiple species, such as pro-nuclei, spindle, and spindle-poles. …”
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Micro-Mobility Safety Assessment: Analyzing Factors Influencing the Micro-Mobility Injuries in Michigan by Mining Crash Reports by Baraah Qawasmeh, Jun-Seok Oh, Valerian Kwigizile

“…In addition, the findings emphasize the overall effect of many different variables, such as improper lane use, violations, and hazardous actions by micro-mobility users. …”
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Citrus Disease Classification Model Based on Improved ConvNeXt by Jichi Yan, Yongbin Mo, Yannan Yu, Shiqing Dou, Rongfeng Yang

“…Secondly, the multi-scale feature fusion module is incorporated to improve the model’s adaptability to disease features at different scales and improve the network classification performance. …”
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Hybrid mechanism‐data‐driven iron loss modelling for permanent magnet synchronous motors considering multiphysics coupling effects by Lin Liu, Wenliang Yin, Youguang Guo

“…Subsequently, a convolutional neural network (CNN) algorithm is employed to perform deep learning to extract features and patterns from the data. …”
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Swin Transformer With Late-Fusion Feature Aggregation for Multi-Modal Vehicle Reidentification by Reza Fuad Rachmadi, Supeno Mardi Susiki Nugroho, I. Ketut Eddy Purnama

“…Further analysis using t-SNE and GradCAM visualization shows that our proposed classifier can effectively distinguish different vehicle IDs by extracting strong features, with the headlight and backlight of the vehicle being the main regions extracted in the SAFA network.…”
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Mobile detection of cataracts with an optimised lightweight deep Edge Intelligent technique by Dipta Neogi, Mahirul Alam Chowdhury, Mst. Moriom Akter, Md. Ishan Arefin Hossain

“…This is done by comparing different models and optimisers. Using these methods, a reliable model can be obtained that detects cataracts. …”
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Leveraging artificial intelligence for diagnosis of children autism through facial expressions by Mahmood A. Mahmood, Leila Jamel, Nazik Alturki, Medhat A. Tawfeek

“…The ViT-ResNet152 model’s convolutional and transformer processing elements worked together to improve the accuracy of the diagnosis to 91.33% and make it better at finding different cases of autism spectrum disorder (ASD).The research outcomes demonstrate that AI tools show promise for delivering highly precise and standardized methods to detect ASD at an early stage. …”
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Resilience driven EV coordination in multiple microgrids using distributed deep reinforcement learning by Yuxin Wu, Ting Cai, Xiaoli Li

“…Simulation results implemented on the modified IEEE 33-bus test feeder demonstrate that AD-MADDPG outperforms all other baselines in terms of load restoration, restoration fairness, and energy consumption when varying different numbers of EVs, maximum discharging proportion, and maximum moving distance.…”
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Autonomous Quadrotor Path Planning Through Deep Reinforcement Learning With Monocular Depth Estimation by Mahdi Shahbazi Khojasteh, Armin Salimi-Badr

“…The former module uses a convolutional encoder-decoder network to learn image depth from visual cues self-supervised, with the output serving as input for the latter module. …”
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Deep Learning in Wireless Communication Receivers: A Survey by Shadman Rahman Doha, Ahmed Abdelhadi

“…The survey not only emphasizes the potential of deep learning-based receivers in future wireless communication but also highlights different challenges of deep learning-based receivers, such as data availability, security and privacy concerns, model interpretability, computational complexity, and integration with legacy systems.…”
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Application of LiDAR and SLAM Technologies in Autonomous Systems for Precision Grapevine Pruning and Harvesting by Obaid Saif, Mohammed G., Nagaraju Krishna Chythanya

“…RGB-D cameras capture visual and depth information of grapevines and fruits, while CNNs process this data to classify different vines and grapes, enabling focused pruning and harvesting decisions. …”
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UETT4K Anti-UAV: A Large Scale 4K Benchmark Dataset for Vision-Based Drone Detection in High-Resolution Imagery by Mughees Sarwar Awan, Syed Azhar Ali Zaidi, Junaid Mir

“…The dataset is created by obtaining real-world videos of different types of drones in diverse environmental and challenging conditions. …”
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Regional Short‐Term Wind Power Prediction Based on CEEMDAN‐FTC Feature Mapping and EC‐TCN‐BiLSTM Deep Learning by Guoyuan Qin, Xiaosheng Peng, Zimin Yang

“…First, the regional input features, encompassing data from numerous wind farms, are decomposed using the CEEMDAN algorithm to extract intrinsic mode functions (IMFs) and residuals at different time scales. Second, the decomposed IMFs and residuals are reconstructed using the adaptive FTC feature mapping technique, forming a high‐dimensional feature set in the time‐frequency domain, which boasts fewer features than the original set, thus diminishing the computational intricacy of the prediction model. …”
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Modeling energy consumption indexes of an industrial cement ball mill for sustainable production by Saeed Chehreh Chelgani, Rasoul Fatahi, Ali Pournazari, Hamid Nasiri

“…To fill the gap, this study developed a CL by examining different AI models (Random Forest, Support Vector Regression, Convolutional Neural Network, extreme gradient boosting, CatBoost, and SHapley Additive exPlanations) for modeling energy consumption indexes of a close ball mill circuit in a cement plant to address the effectiveness of operating variables. …”
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Comparative analysis of deep learning and machine learning models for one-day-ahead streamflow forecasting in the Krishna River basin by Sukhsehaj Kaur, Sagar Rohidas Chavan

“…A comprehensive evaluation of eleven models was conducted to assess their strengths and limitations across different datasets. New hydrological insights: The study implemented Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Gated Recurrent Unit (GRU), Bidirectional GRU, Convolutional Neural Network, WaveNet, K-Nearest Neighbours, Random Forest (RF), Support Vector Regression, Adaptive Boosting, and Extreme Gradient Boosting (XGBoost) to forecast streamflow at each site. …”
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