PM2.5 Forecasting at U.S. Embassies and Consulates Worldwide Using NASA Model Powered by Machine Learning by Junhyeon Seo, Alqamah Sayeed, Seohui Park, John Kerekes, Stephanie M. Christel, Mary T. Tran, Pawan Gupta

“…Hybrid monitoring (surface, satellite, and models) offers a scalable, cost‐effective solution for tracking pollution and trends. …”
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Improving SOC estimation in low-relief farmlands using time-series crop spectral variables and harmonic component variables based on minimum sample size by Chenjie Lin, Ling Zhang, Nan Zhong

“…Additionally, few studies have considered the sample size in modeling SOC estimation, which may lead to precision loss and cost waste. Therefore, this study proposed a novel method to improve SOC estimation in low-relief farmlands. …”
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Machine learning frameworks to accurately predict coke reactivity index by Ayat Hussein Adhab, Morug Salih Mahdi, Krunal Vaghela, Anupam Yadav, Jayaprakash B, Mayank Kundlas, Ankur Srivastava, Jayant Jagtap, Aseel Salah Mansoor, Usama Kadem Radi, Nasr Saadoun Abd, Samim Sherzod

“…Precisely forecasting coke reactivity index (CRI) plays a critical role in the metallurgical industry, as it enables optimization of coke quality, leading to cost-effective production and efficient resource utilization. …”
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Explainable MRI-Based Ensemble Learnable Architecture for Alzheimer’s Disease Detection by Opeyemi Taiwo Adeniran, Blessing Ojeme, Temitope Ezekiel Ajibola, Ojonugwa Oluwafemi Ejiga Peter, Abiola Olayinka Ajala, Md Mahmudur Rahman, Fahmi Khalifa

“…Many approaches presented in the AI and medical literature for overcoming this critical weakness are sometimes at the cost of sacrificing accuracy for interpretability. …”
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A Study on the Lightweight and Fast Response GRU Techniques for Indoor Continuous Motion Recognition Based on Wi-Fi CSI by Kyongseok Jang, Chao Sun, Junhao Zhou, Yongbin Seo, Youngok Kim, Seyeong Choi

“…The proposed LFR-GRU model is evaluated using criteria such as F1-score, total number of parameters, learning time, processing speed, and memory usage, and its performance is compared with conventionally available models like 1D convolutional neural networks (CNNs), long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and Bi-GRU. …”
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Joint Adaptive Resolution Selection and Conditional Early Exiting for Efficient Video Recognition on Edge Devices by Qingli Wang, Chengwu Yu, Shan Chen, Weiwei Fang, Naixue Xiong

“…Deep learning has shown its remarkable performance in video analytics, by applying 2D or 3D Convolutional Neural Networks (CNNs) across multiple video frames. …”
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A New Modeling Method for Meteorological Information of Regional Distributed Photovoltaic Power Generation Based on Multi‐Source Information Fusion by Yuhang Wang, Dengxuan Li, Wenwen Ma, Xi Zhang, Honglu Zhu

“…Consequently, developing effective, reliable, and cost‐efficient meteorological information computation methods for DPV has become a critical research focus. …”
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A Method for Recognizing Dead Sea Bass Based on Improved YOLOv8n by Lizhen Zhang, Chong Xu, Sai Jiang, Mengxiang Zhu, Di Wu

“…Second, the C2f-faster–EMA (efficient multi-scale attention) convolutional module was designed to replace the C2f module in the backbone network of YOLOv8n, reducing redundant calculations and memory access, thereby more effectively extracting spatial features. …”
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Assessment of Vegetation Indices Derived from UAV Imagery for Weed Detection in Vineyards by Fabrício Lopes Macedo, Humberto Nóbrega, José G. R. de Freitas, Miguel A. A. Pinheiro de Carvalho

“…., Random Forest and Convolutional Neural Networks) to enhance robustness and generalization. …”
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Accelerating Deep Learning-Based Morphological Biometric Recognition with Field-Programmable Gate Arrays by Nourhan Zayed, Nahed Tawfik, Mervat M. A. Mahmoud, Ahmed Fawzy, Young-Im Cho, Mohamed S. Abdallah

“…Convolutional neural networks (CNNs) are increasingly recognized as an important and potent artificial intelligence approach, widely employed in many computer vision applications, such as facial recognition. …”
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Enhancement and evaluation for deep learning-based classification of volumetric neuroimaging with 3D-to-2D knowledge distillation by Hyemin Yoon, Do-Young Kang, Sangjin Kim

“…Abstract The application of deep learning techniques for the analysis of neuroimaging has been increasing recently. The 3D Convolutional Neural Network (CNN) technology, which is commonly adopted to encode volumetric information, requires a large number of datasets. …”
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YOLOv8-MFD: An Enhanced Detection Model for Pine Wilt Diseased Trees Using UAV Imagery by Hua Shi, Yonghang Wang, Xiaozhou Feng, Yufen Xie, Zhenhui Zhu, Hui Guo, Guofeng Jin

“…The model incorporates a MobileViT-based backbone that fuses convolutional neural networks with Transformer-based global modeling to enhance feature representation under complex forest backgrounds. …”
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Tunable Energy-Efficient Approximate Circuits for Self-Powered AI and Autonomous Edge Computing Systems by Shubham Garg, Kanika Monga, Nitin Chaturvedi, S. Gurunarayanan

“…However, the accuracy in computations came with an additional cost of increased computational resources and power consumption in traditional computing units. …”
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Harnessing clinical annotations to improve deep learning performance in prostate segmentation. by Karthik V Sarma, Alex G Raman, Nikhil J Dhinagar, Alan M Priester, Stephanie Harmon, Thomas Sanford, Sherif Mehralivand, Baris Turkbey, Leonard S Marks, Steven S Raman, William Speier, Corey W Arnold

“…<h4>Purpose</h4>Developing large-scale datasets with research-quality annotations is challenging due to the high cost of refining clinically generated markup into high precision annotations. …”
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Wheat Leaf Disease Detection: A Lightweight Approach with Shallow CNN Based Feature Refinement by Oumayma Jouini, Mohamed Ould-Elhassen Aoueileyine, Kaouthar Sethom, Anis Yazidi

“…In this paper, we propose CropNet, a hybrid method that utilizes Red, Green, and Blue (RGB) imaging and a transfer learning approach combined with shallow convolutional neural networks (CNN) for further feature refinement. …”
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Precision in practice: exploring the impact of ai and machine learning on ultrasound guided regional anaesthesia by Noor Ul Huda Bhatti, Syed Ghazi Ali Kirmani, Maryam Butt

“…Its benefits include, being non-invasive, cost-effective, readily accessible, and providing the anaesthetist with clear visualization of essential anatomical landmarks, needle progression, and the spread of local anaesthetic. …”
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Design and Research on a Reed Field Obstacle Detection and Safety Warning System Based on Improved YOLOv8n by Yuanyuan Zhang, Zhongqiu Mu, Kunpeng Tian, Bing Zhang, Jicheng Huang

“…Additionally, we employ a Lightweight Shared Convolutional Separable Batch Normalization Detection Head in the detection head, which significantly reduces the number of parameters while improving detection accuracy. …”
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Revolutionizing sleep disorder diagnosis: A Multi-Task learning approach optimized with genetic and Q-Learning techniques by Soraya Khanmohmmadi, Toktam Khatibi, Golnaz Tajeddin, Elham Akhondzadeh, Amir Shojaee

“…The study proposes an innovative multi-task learning convolutional neural network with a partially shared structure that uses frequency-time images generated from EEG signals to address these limitations. …”
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Deep Learning in Defect Detection of Wind Turbine Blades: A Review by Katleho Masita, Ali N. Hasan, Thokozani Shongwe, Hasan Abu Hilal

“…Key advancements are highlighted, including the integration of Convolutional Neural Networks (CNNs), Variational Autoencoders (VAEs), and Generative Adversarial Networks (GANs) for image-based detection and anomaly identification. …”
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SGSNet: a lightweight deep learning model for strawberry growth stage detection by Zhiyu Li, Jianping Wang, Guohong Gao, Yufeng Lei, Chenping Zhao, Yan Wang, Haofan Bai, Yuqing Liu, Xiaojuan Guo, Qian Li

“…An innovative lightweight convolutional neural network, named GrowthNet, is designed as the backbone of SGSNet, facilitating efficient feature extraction while significantly reducing model parameters and computational complexity. …”
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