An Optimized Model for Identification of Cerebral Palsy Using Deep Learning by Md Anjar Ahsan, Hassan bin Mohammed, Abhilash Maroju, Nurhafizah Moziyana Mohd Yusop, Wan Su Emi Yusnita Wan Yusof, Najjah Salwa Abd Razak, Mohd Arif Dar

“…This research uses a deep convolutional neural network (CNN) based on a modified AlexNet architecture to classify CP subtypes using newborn fMRI data. …”
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Assessment of the Diagnostic Accuracy of Artificial Intelligence Software in Identifying Common Periodontal and Restorative Dental Conditions (Marginal Bone Loss, Periapical Lesion... by Wael I. Ibraheem, Saurabh Jain, Mohammed Naji Ayoub, Mohammed Ahmed Namazi, Amjad Ismail Alfaqih, Aparna Aggarwal, Abdullah A. Meshni, Ammar Almarghlani, Abdulkareem Abdullah Alhumaidan

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Physical bases for determination of scattering kernels from incomplete data in grid-less X-ray imaging by A. Yu. Danyk, O. O. Sudakov

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MCAF-Net: Multi-Channel Temporal Cross-Attention Network with Dynamic Gating for Sleep Stage Classification by Xuegang Xu, Quan Wang, Changyuan Wang, Yaxin Zhang

“…To overcome these shortcomings, we present MCAF-Net, a novel network architecture that employs temporal convolution modules to extract channel-specific features from each input signal and introduces a dynamic gated multi-head cross-channel attention mechanism (MCAF) to effectively model the interdependencies between different physiological channels. …”
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Improving YOLOv11 for marine water quality monitoring and pollution source identification by Fang Wang

“…The Marine Fusion Loss (MFL) mechanism optimizes detection weight allocation among different pollutant categories, reducing false positives. …”
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Lightweight multiscale information aggregation network for land cover land use semantic segmentation from remote sensing images by Yahia Said, Oumaima Saidani, Ali Delham Algarni, Mohammad H. Algarni, Ayman Flah

“…This paper presents a lightweight neural network designed to address these challenges by integrating dense dilated convolutions with pyramid depthwise convolutions for multiscale feature extraction. …”
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SFPFMformer: Short-Term Power Load Forecasting for Proxy Electricity Purchase Based on Feature Optimization and Multiscale Decomposition by Chengfei Qi, Yanli Feng, Junling Wan, Xinying Mao, Peisen Yuan

“…Finally, we utilize a depthwise separable convolution block to extract features from power load data, which efficiently captures the pattern of change in load. …”
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End‐To‐End Deep Learning Temperature Prediction Algorithms of a Phase Change Materials From Experimental Photos by Mohammad Hassan Ranjbar, Kobra Gharali, Artie Ng

“…Initially, the networks were built using different convolutional layers and weights for feature extraction, and then the fully connected layers extracted the temperature profiles of the PCM. …”
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LM-CNN-based Automatic Cost Calculation Model for Power Transmission and Transformation Projects by Xiaolin WU, Ling LUAN, Lianwu PAN, Hailong LI

“…Finally, in view of the big difference between the expected output and the actual output, the Levenberg-Marquart algorithm is utilized to optimize the weight parameters of the convolutional neural network to complete the model training. …”
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Research of Fault Feature Extraction and Diagnosis of Planetary Gear Train based on SPS and CNN by Pan Zheng, Jianhua Zhou, Sujie Gao, Ben Chen, Xiangxiong Liu, Shijing Wu

“…The vibration signals of the planetary gearbox under different operating conditions and different fault states are further collected，and the extracted fault features are input into the convolutional neural network for fault identification. …”
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Exoplanet Classification Through Vision Transformers with Temporal Image Analysis by Anupma Choudhary, Sohith Bandari, B. S. Kushvah, C. Swastik

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Dynamic deep learning based super-resolution for the shallow water equations by Maximilian Witte, Fabrício R Lapolli, Philip Freese, Sebastian Götschel, Daniel Ruprecht, Peter Korn, Christopher Kadow

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Apple Watercore Grade Classification Method Based on ConvNeXt and Visible/Near-Infrared Spectroscopy by Chunlin Zhao, Zhipeng Yin, Yushuo Tan, Wenbin Zhang, Panpan Guo, Yaxing Ma, Haijian Wu, Ding Hu, Quan Lu

“…To address the issues of insufficient rigor in existing methods for quantifying apple watercore severity and the complexity and low accuracy of traditional classification models, this study proposes a method for watercore quantification and a classification model based on a deep convolutional neural network. Initially, visible/near-infrared transmission spectral data of apple samples were collected. …”
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Application of machine learning for brain tumor diagnosis using magnetic resonance images: a comparative analysis by Patel Rahul kumar-Manilal, D. J. Shah

“…The study described applies and evaluates three different methods. The study applied and evaluated three different methods for identifying brain tumors: a self-defined a support vector machine (SVM), a Random forest (RF), and a convolution neural network (CNN). …”
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Deep Learning Based Breast Cancer Detection Using Decision Fusion by Doğu Manalı, Hasan Demirel, Alaa Eleyan

“…First, SVM distinguishes between different tumor types using local binary pattern (LBP) features. …”
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Super‐resolution reconstruction algorithm for medical images by fusion of wavelet transform and multi‐scale adaptive feature selection by QiaoSu Wang, Qiaomei Ma

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Blind Interleaver Recognition Using Deep Learning Techniques by Nayim Ahamed, Swaminathan R., B. Naveen

“…This paper explores the application of deep learning to recognize four different interleavers such as block, convolutional, helical, and random especially in non-cooperative environments. …”
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Deep Learning Integrating Scale Conversion and Pedo‐Transfer Function to Avoid Potential Errors in Cross‐Scale Transfer by Peijun Li, Yuanyuan Zha, Yonggen Zhang, Chak‐Hau Michael Tso, Sabine Attinger, Luis Samaniego, Jian Peng

“…The proposed method uses the convolutional neural network (CNN) as a cross‐scale transfer approach to directly map soil/landscape static properties to soil hydraulic parameters across different spatial scales. …”
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Denoising of Heart Sounds Using Lightweight FCNs and Spectrograms With and Without Context by Declan Duggan, Andriy Temko, Volodymyr Sarana, Andreea Factor, Emanuel Popovici

“…We propose a method for denoising heart sounds using fully convolutional networks (FCNs) based on the Spleeter U-Net architecture. …”
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Environmental sound classification system by I. N. Zhuk

“…Classification model includes classic convolutional neuron network architectures. Experiments based on different architectures of convolutional neural networks and proposed feature extraction method. …”
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