Optimising Concrete Crack Detection: A Study of Transfer Learning with Application on Nvidia Jetson Nano by C. Long Nguyen, Andy Nguyen, Jason Brown, Terry Byrne, Binh Thanh Ngo, Chieu Xuan Luong

“…One deployment scenario involves using a drone to carry an embedded device and camera, with the device making localised predictions at the edge about the existence of defects using a trained convolutional neural network (CNN) for image classification. …”
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Surface Classification from Robot Internal Measurement Unit Time-Series Data Using Cascaded and Parallel Deep Learning Fusion Models by Ghaith Al-refai, Dina Karasneh, Hisham Elmoaqet, Mutaz Ryalat, Natheer Almtireen

“…The first model, a cascaded fusion model, employs a 1-D Convolutional Neural Network (CNN) followed by a Long Short-Term Memory (LSTM) network and then a multi-head attention mechanism. …”
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Enhanced Workload Prediction in Data Centers Using Two-Stage Decomposition and Hybrid Parallel Deep Learning by Dalal Alqahtani, Hamidreza Imani, Tarek El-Ghazawi

“…Workload prediction is one of the most basic requirements in developing cost and energy-efficient Cloud Data Centers (CDCs). …”
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Deep Learning for Urban Tree Canopy Coverage Analysis: A Comparison and Case Study by Grayson R. Morgan, Danny Zlotnick, Luke North, Cade Smith, Lane Stevenson

“…However, most studies have tested only one or two classification methods to accomplish this while using costly satellite imagery or LiDAR data. This study seeks to compare three urban tree canopy cover classifiers by testing a deep learning U-Net convolutional neural network (CNN), support vector machine learning classifier (SVM) and a random forests machine learning classifier (RF) on cost-free 2012 aerial imagery over a small southern USA city and midsize, growing southern USA city. …”
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Advances in the Automated Identification of Individual Tree Species: A Systematic Review of Drone- and AI-Based Methods in Forest Environments by Ricardo Abreu-Dias, Juan M. Santos-Gago, Fernando Martín-Rodríguez, Luis M. Álvarez-Sabucedo

“…Findings of this study reveal that deep learning (DL) models, particularly convolutional neural networks (CNN), are increasingly replacing traditional ML methods such as random forest (RF) or support vector machines (SVMs) because there is no need for a feature extraction phase, as this is implicit in the DL models. …”
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Few-Shot Metric Learning with Time-Frequency Fusion for Specific Emitter Identification by Shiyuan Mu, Yong Zu, Shuai Chen, Shuyuan Yang, Zhixi Feng, Junyi Zhang

“…To enhance the discriminative capability for radiation source signals, the model employs a convolutional block attention module (CBAM) and feature transformation to effectively fuse the raw signal’s time domain and time-frequency domain representations. …”
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Deep Learning in Airborne Particulate Matter Sensing and Surface Plasmon Resonance for Environmental Monitoring by Balendra V. S. Chauhan, Sneha Verma, B. M. Azizur Rahman, Kevin P. Wyche

“…DL techniques, such as convolutional neural networks (CNNs), autoencoders, recurrent neural networks (RNNs), and their variants, are examined for applications like PM estimation from satellite data, air quality prediction, and sensor calibration. …”
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Meta-RHDC: Meta Reinforcement Learning Driven Hybrid Lyrebird Falcon Optimization for Dynamic Load Balancing in Cloud Computing by Mallu Shiva Rama Krishna, D. Khasim Vali

“…Cloud computing offers a scalable and cost-effective platform by providing on-demand access to shared computational resources. …”
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Study of Recent Image Restoration Techniques: A Comprehensive Survey by Nikita Singhal, Anup Kadam, Pravesh Kumar, Hritik Singh, Aaryan Thakur, Pranay

“…Recent advances in machine learning (ML), especially deep learning (DL) using convolutional neural networks (CNNs), have made data-driven approaches that learn directly from large datasets much more effective. …”
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Beyond the Backbone: A Quantitative Review of Deep-Learning Architectures for Tropical Cyclone Track Forecasting by He Huang, Difei Deng, Liang Hu, Yawen Chen, Nan Sun

“…We categorize all DL-based TC tracking models according to the architecture, including recurrent neural networks (RNNs), convolutional neural networks (CNNs), Transformers, graph neural networks (GNNs), generative models, and Fourier-based operators. …”
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Enhancing Periodontal Bone Loss Diagnosis Through Advanced AI Techniques by Nader Nabil Fouad Rezallah, George Sherif, Ahmed Z. Abdelkarim, Shereen Afifi

“…Based on our extensive research, we concluded that convolutional neural networks (CNNs) are the most effective type of neural network for addressing our problem. …”
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GazeMap: Dual-Pathway CNN Approach for Diagnosing Alzheimer’s Disease from Gaze and Head Movements by Hyuntaek Jung, Shinwoo Ham, Hyunyoung Kil, Jung Eun Shin, Eun Yi Kim

“…This study proposes a novel AD detection framework integrating gaze and head movement analysis via a dual-pathway convolutional neural network (CNN). Unlike conventional methods relying on linguistic, speech, or neuroimaging data, our approach leverages non-invasive video-based tracking, offering a more accessible and cost-effective solution to early AD detection. …”
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Development of Risk Activity Detection System for Forklifts Based on Inertial Sensors by Luciano Radrigan, Sebastian E. Godoy

“…In this paper, we developed convolutional neural networks (CNN) and long-term memory (LSTM) algorithms to infer a risky maneuver from the inertial sensors data and compared it to the outcome of a video-based model trained on data labeled by a risk-prevention engineer. …”
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Utilization of Neural Network in the Diagnosis of Pes Planus and Pes Cavus with a Smartphone Camera by Samir Ghandour MD, Anton Lebedev BS, Wei Shao Tung BS, Konstantin Semianov BS, Artem Semyanov MS, Daniel Guss MD, MBA, Gregory R. Waryasz MD, John Y. Kwon MD, Christopher W. DiGiovanni MD, Soheil Ashkani-Esfahani MD, Lorena Bejarano-Pineda MD

“…Methods: An algorithm that integrated a deep learning, convolutional neural network (CNN) into a smartphone camera was utilized to detect Pes planus and Pes cavus deformities. …”
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RT-DETR-Smoke: A Real-Time Transformer for Forest Smoke Detection by Zhong Wang, Lanfang Lei, Tong Li, Xian Zu, Peibei Shi

“…First, we designed a high-efficiency hybrid encoder that combines convolutional and Transformer features, thus reducing computational cost while preserving crucial smoke details. …”
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IMViT: Adjacency Matrix-Based Lightweight Plain Vision Transformer by Qihao Chen, Yunfeng Yan, Xianbo Wang, Jishen Peng

“…While extensive experiments prove its outstanding ability for large models, transformers with small sizes are not comparable with convolutional neural networks in various downstream tasks due to its lack of inductive bias which can benefit image understanding. …”
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A review on artificial intelligence thermal fluids and the integration of energy conservation with blockchain technology by Abdullah Ayub Khan, Asif Ali Laghari, Syed Azeem Inam, Sajid Ullah, Laila Nadeem

“…Furthermore, diverse reinforcement learning techniques facilitate the adoptive control of intricate thermal applications in real-time settings, while Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are employed for applicational monitoring and real-time data processing. …”
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DEPANet: A Differentiable Edge-Guided Pyramid Aggregation Network for Strip Steel Surface Defect Segmentation by Yange Sun, Siyu Geng, Chengyi Zheng, Chenglong Xu, Huaping Guo, Yan Feng

“…The steel strip is an important and ideal material for the automotive and aerospace industries due to its superior machinability, cost efficiency, and flexibility. However, surface defects such as inclusions, spots, and scratches can significantly impact product performance and durability. …”
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Video swin-CLSTM transformer: Enhancing human action recognition with optical flow and long-term dependencies. by Jun Qin, Shenwei Chen, Zheng Ye, Jing Liu, Zhou Liu

“…Additionally, by embedding Convolutional Long Short-Term Memory (ConvLSTM) units, the model's capacity to capture and understand long-term dependencies among key actions in videos is further enhanced. …”
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Unmanned Aerial Vehicle-Based Hyperspectral Imaging for Potato Virus Y Detection: Machine Learning Insights by Siddat B. Nesar, Paul W. Nugent, Nina K. Zidack, Bradley M. Whitaker

“…The performance of the models is promising, with the convolutional neural network (CNN) achieving a recall of 0.831, reliably identifying the PVY-infected plants. …”
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