Advancements in Plant Pests Detection: Leveraging Convolutional Neural Networks for Smart Agriculture by Gopalakrishnan Nagaraj, Dakshinamurthy Sungeetha, Mohit Tiwari, Vandana Ahuja, Ajit Kumar Varma, Pankaj Agarwal

“…This article presents a summary of three perspectives, each of which is based on a different network design, in recent research on deep learning applied to the detection of plant diseases and pests. We developed a convolutional neural network (CNN)-based framework for identifying pest-borne diseases in tomato leaves using the Plant Village Dataset and the MobileNetV2 architecture. …”
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Immune status assessment based on plasma proteomics with meta graph convolutional networks by Min Zhang, Nan Xu, Qi Cheng, Jing Ye, Shiwei Wu, Haoliang Liu, Chengkui Zhao, Lei Yu, Weixing Feng

“…This framework identified 309 immune-related factors with associated biological functions and pathways. …”
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Under-scanning non-line-of-sight imaging based on convolution approximation and optimization by Jinye Miao, Yingjie Shi, Lingfeng Liu, Yi Wei, Fuyao Cai, Lianfa Bai, Enlai Guo, Jing Han

“…Even with an extremely short exposure time of 1.28 s, DO-NLOS can still distinguish objects with a distance of 6 cm, which is close to the axial resolution limit of the system. Our framework has great potential in the application of real-time scanning NLOS imaging.…”
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3D long time spatiotemporal convolution for complex transfer sequence prediction by Qiu Yunan, Cui Yingjie, Tang Haibo, Chen Zhongfeng, Lu Zhenyu, Xue Feng

“…In order to solve the above problems, we propose 3DcT-Pred based on the existing mainstream end-to-end modeling framework and combined with two-branch 3D convolution.Specifically, the proposed model first mitigates the long-range forgetting problem by extracting long-term global features of spatio-temporal sequence data. …”
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Multistation Wind Speed Forecasting Based on Dynamic Spatiotemporal Graph Convolutional Networks by Jianhong Gan, Runqing Kang, Xun Deng, Chentao Mao, Zhibin Li, Peiyang Wei, Chunjiang Wu, Tongli He

“…Next, we introduce a novel spatiotemporal feature extraction framework, which employs residual graph convolutional networks combined with a multihead attention mechanism to extract spatial features. …”
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Material Segmentation Using 1-D Convolutional Neural Network With Transient Histogram by Yohanssen Pratama, Kazuya Kitano, Takahiro Kushida, Yuki Fujimura, Takuya Funatomi, Yasuhiro Mukaigawa

“…These findings highlight the potential of SPAD sensors combined with advanced classification techniques to enhance material classification and segmentation, providing a versatile framework for applications in robotics, computer vision, and optical sensing.…”
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Using deep convolutional neural networks to forecast spatial patterns of Amazonian deforestation by James G. C. Ball, Katerina Petrova, David A. Coomes, Seth Flaxman

“…The modelling framework can be readily applied to any tropical forest location and used by governments and conservation organisations to prevent deforestation and plan protected areas.…”
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Learning to Make Document Context-Aware Recommendation with Joint Convolutional Matrix Factorization by Lei Guo, Yu Han, Haoran Jiang, Xinxin Yang, Xinhua Wang, Xiyu Liu

“…Based on the above observations, in this work, we target CR and propose a joint convolutional matrix factorization (JCMF) method to tackle the encountered challenges, which jointly considers the item’s reviews, item’s relationships, user’s social influence, and user’s reviews in a unified framework. …”
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Risk assessment of thyroid nodules with a multi-instance convolutional neural network by Da Yu, Tingting Song, Yancheng Yu, Hebin Zhang, Feng Gao, Zirong Wang, Jiacheng Wang

“…However, existing AI-assisted methods often suffer from limited diagnostic performance.MethodsIn this study, we propose a novel multi-instance learning (MIL) convolutional neural network (CNN) model tailored for ultrasound-based thyroid cancer diagnosis. …”
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Dose Reduction in Scintigraphic Imaging Through Enhanced Convolutional Autoencoder-Based Denoising by Nikolaos Bouzianis, Ioannis Stathopoulos, Pipitsa Valsamaki, Efthymia Rapti, Ekaterini Trikopani, Vasiliki Apostolidou, Athanasia Kotini, Athanasios Zissimopoulos, Adam Adamopoulos, Efstratios Karavasilis

“…Methods: A supervised learning framework was developed using real-world paired low- and full-dose images from 105 patients. …”
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PERFORMANCE REFINEMENT OF CONVOLUTIONAL NEURAL NETWORK ARCHITECTURES FOR SOLVING BIG DATA PROBLEMS by Saud Aljaloud

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Deep convolution neural network model in problem of crack segmentation on asphalt images by B. V. Sobol, A. N. Soloviev, P. V. Vasiliev, L. A. Podkolzina

“…Keras and TensorFlow frameworks were used for the software implementation of the proposed architecture.Research Results. …”
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SLiTRANet: An EEG-Based Automated Diagnosis Framework for Major Depressive Disorder Monitoring Using a Novel LGCN and Transformer-Based Hybrid Deep Learning Approach by Sagnik de, Anurag Singh, Vivek Tiwari, Harshita Patel, G. N. Vivekananda, Dharmendra Singh Rajput

“…This work proposes an EEG-headset-based smart monitoring system for real-time diagnosis of MDD in the Internet of Medical Things (IoMT) framework. In this study, we proposed a novel Linear Graph Convolution Network-Transformer-based deep learning approach for categorizing MDD through a time-frequency analysis of EEG signals. …”
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A Framework for Flood Disaster Detection From Remote Sensing Images Using Spatiotemporal Fusion With Digital Twin Technology by Se-Jung Lim, K. Sakthidasan Sankaran, Anandakumar Haldorai

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HFF-Net: A hybrid convolutional neural network for diabetic retinopathy screening and grading by Muhammad Hassaan Ashraf, Hamed Alghamdi

“…While Computer-Aided Diagnosis (CAD) systems can alleviate this burden, existing Convolutional Neural Network (CNN)-based frameworks use fixed-size kernels in a linear feed-forward manner. …”
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A Dual Convolution Model for Grating Lobe Prediction in Directivity of Parametric Array Loudspeakers by Shangming Mei, Yihua Hu, Mohammed Alkahtani, Mohammad Nasr Esfahani

“…Within this framework, a pioneering Dual Convolutional Model is introduced to account for critical parameters of the phased array. …”
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MagNet: Automated Magnetic Mineral Grain Morphometry Using Convolutional Neural Network by Zhaowen Pei, Liao Chang, Pengfei Xue, Richard J. Harrison

“…This framework, based on a convolutional neural network, performs well in the recognition and classification of magnetofossil nanoparticles in transmission electron microscopy images after training and testing. …”
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Enhanced neurological anomaly detection in MRI images using deep convolutional neural networks by Ahmed Mateen Buttar, Zubair Shaheen, Abdu H. Gumaei, Mogeeb A. A. Mosleh, Mogeeb A. A. Mosleh, Indrajeet Gupta, Samah M. Alzanin, Muhammad Azeem Akbar

“…This study introduces a deep learning framework designed to automate neuro-diagnostics, addressing the limitations of current manual interpretation methods, which are often time-consuming and prone to variability.MethodsWe propose a specialized deep convolutional neural network (DCNN) framework aimed at detecting and classifying neurological anomalies in MRI data. …”
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An intelligent attention based deep convoluted learning (IADCL) model for smart healthcare security by J. Maruthupandi, S. Sivakumar, B. Lakshmi Dhevi, S. Prasanna, R. Karpaga Priya, Shitharth Selvarajan

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Hydrogen reaction rate modeling based on convolutional neural network for large eddy simulation by Quentin Malé, Corentin J. Lapeyre, Nicolas Noiray

“…This article establishes a data-driven modeling framework for lean hydrogen ( $ {\mathrm{H}}_2 $ )-air reaction rates for the Large Eddy Simulation (LES) of turbulent reactive flows. …”
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