Hand Gesture Recognition in Indian Sign Language Using Deep Learning by Harsh Kumar Vashisth, Tuhin Tarafder, Rehan Aziz, Mamta Arora, Alpana

“…This is achieved by using and implementing Convolutional Neural Networks on our self-made dataset. …”
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Balancing Data Privacy and 5G VNFs Security Monitoring: Federated Learning with CNN + BiLSTM + LSTM Model by Abdoul-Aziz Maiga, Edwin Ataro, Stanley Githinji

“…Another fact is that many VNFs vendors with different security policies will be implied in 5G deployment, creating a heterogeneous 5G network. …”
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Self-Supervised Keypoint Learning for the Geometric Analysis of Road-Marking Templates by Chayanon Sub-r-pa, Rung-Ching Chen

“…To address this, we propose GeoTemplateKPNet, a novel self-supervised deep-learning framework, built upon Convolutional Neural Networks (CNNs), designed to learn robust, geometrically consistent keypoints specifically in synthetic template images. …”
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Decoding vocal indicators of stress in laying hens: A CNN-MFCC deep learning framework by Suresh Neethirajan

“…This study leverages advanced Convolutional Neural Networks (CNNs) combined with Mel Frequency Cepstral Coefficients (MFCCs) to decode intricate vocalization patterns in laying hens experiencing acute environmental stress. …”
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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

“…The method combines an attention mechanism with a convolutional neural network (CNN) and long short-term memory (LSTM) network to address the nonlinear nature of multivariate time-series data. …”
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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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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

“…Here, we compare multiple deep convolutional neural networks (CNNs) trained to automate cell stage classification in DIC microscopy movies and interpret the results, with code and classification weights released as OpenSource. …”
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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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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

“…The proposed SAFA classifier is constructed by attaching multi-head networks with CBAM (Convolutional Block Attention Module) and MWN (Modality Weighted Network) to the three parallel shared Swin Transformer architecture for each modality input (visual, near-infrared, and thermal). …”
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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

“…This survey explores various deep learning architectures such as multilayer perceptrons (MLPs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and autoencoders, focusing on their application in the design of wireless receivers. …”
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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

“…This project creates an autonomous system for grapevine pruning and harvesting using LiDAR, SLAM, RGB-D cameras, Convolutional Neural Networks (CNNs), proximity sensors, and Wireless Sensor Networks (WSNs). …”
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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

“…Vision-based approaches, especially those employing deep convolutional neural networks (DCNNs), show great promise in addressing the need for an accurate and cost-effective drone detection system. …”
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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

“…To improve the accuracy of regional short‐term WPP, a method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), fine‐to‐coarse (FTC) feature mapping, and error compensation‐temporal convolutional network‐bidirectional Long short‐term memory network (EC‐TCN‐BiLSTM) is proposed in this paper. …”
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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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