An Insight on the Timely Diagnosis of Diabetic Retinopathy Using Traditional and AI-Driven Approaches by Malaika Asif, Fasih Ur Rehman, Zoya Rashid, Altaf Hussain, Alina Mirza, Waqar Shahid Qureshi

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EcoTaskSched: a hybrid machine learning approach for energy-efficient task scheduling in IoT-based fog-cloud environments by Asfandyar Khan, Faizan Ullah, Dilawar Shah, Muhammad Haris Khan, Shujaat Ali, Muhammad Tahir

“…The proposed hybrid model integrates Convolutional Neural Networks (CNNs) with Bidirectional Log-Short Term Memory (BiLSTM) to enhance energy-efficient schedulability and reduce energy usage while ensuring QoS provisioning. …”
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Development and Validation of a Deep Learning System for the Detection of Nondisplaced Femoral Neck Fractures by Lianxin Wang, Ce Zhang, Yaozong Wang, Xin Yue, Yunbang Liang, Naikun Sun

“…Hip fractures pose a significant challenge to healthcare systems due to their high costs and associated mortality rates, with femoral neck fractures accounting for nearly half of all hip fractures. …”
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Predicting housing price, housing density, and green area coverage from combined satellite and street view imagery using deep learning by Ana Beatriz de Figueiredo Oliveira, Mauro Castelli, Esra Suel

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Deep Separable Hypercomplex Networks by Nazmul Shahadat, Anthony S. Maida

“…Hypercomplex-inspired networks, however, still incur higher computational costs than standard CNNs. This paper reduces this cost by decomposing a quaternion 2D convolutional module into two consecutive separable vectormap modules. …”
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Handwritten Words Image Character Extraction Adaptive Algorithm Based on the Multi-branch Structure by GUO Xiaojing, ZHAO Xiaoyuan, ZOU Songlin

“…This study applies a new method of multi-branch convolution, the Re-parameterized and Multi-branch Convolution Algorithm (RMCA), to enhance the recognition of complex structures and similar words, improving mean average precision (MAP) and identification efficiency. …”
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A Dense Pyramidal Residual Network with a Tandem Spectral–Spatial Attention Mechanism for Hyperspectral Image Classification by Yunlan Guan, Zixuan Li, Nan Wang

“…In recent years, convolutional neural networks (CNNs) have become a potent tool for hyperspectral image classification (HSIC), where classification accuracy, computational cost, and generalization ability are the main focuses. …”
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A Contrastive Representation Learning Method for Event Classification in Φ-OTDR Systems by Tong Zhang, Xinjie Peng, Yifan Liu, Kaiyang Yin, Pengfei Li

“…Furthermore, CLWTNet incorporates wavelet transform convolution to enhance its capacity to capture intricate features of event signals. …”
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Simplified LSL-Net Architecture for Unmanned Aerial Vehicle Detection in Real-Time by Francisco David Camacho-Gonzalez, Nestor Andres Garcia-Rojas, José Francisco Martínez-Trinidad, Jesús Ariel Carrasco-Ochoa

“…We introduce a simplified LSL-Net architecture using dilated convolutions to achieve a lower-cost architecture with good detection capabilities. …”
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Normalized Difference Vegetation Index Prediction for Blueberry Plant Health from RGB Images: A Clustering and Deep Learning Approach by A. G. M. Zaman, Kallol Roy, Jüri Olt

“…This enhanced clustering accuracy and enabled precise NDVI calculations. A convolutional neural network (CNN) was trained and tested to predict NDVI-based health indices. …”
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A New Way to Identify Mastitis in Cows Using Artificial Intelligence by Rodes Angelo Batista da Silva, Héliton Pandorfi, Filipe Rolim Cordeiro, Rodrigo Gabriel Ferreira Soares, Victor Wanderley Costa de Medeiros, Gledson Luiz Pontes de Almeida, José Antonio Delfino Barbosa Filho, Gabriel Thales Barboza Marinho, Marcos Vinícius da Silva

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An automatic ICD coding method for clinical records based on deep neural network by Yichao DU, Tong XU, Jianhui MA, Enhong CHEN, Yi ZHENG, Tongzhu LIU, Guixian TONG

“…With the increase in the number of the international classification of diseases (ICD) codes,the difficulty and cost of manual coding based on clinical records have greatly increased,and automatic ICD coding technology has attracted widespread attention.A multi-scale residual graph convolution network automatic ICD coding technology was proposed.This technology uses a multi-scale residual network to capture text patterns of different lengths of clinical text and extracts the hierarchical relationship between labels based on the graph convolutional neural network to enhance the ability of automatic coding.The experimental results on the real medical data set MIMIC-III show that the P@k and Micro-F1 of this method are 72.2% and 53.9%,respectively,which significantly improves the prediction performance.…”
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Leveraging Deep Learning for Real-Time Coffee Leaf Disease Identification by Opeyemi Adelaja, Bernardi Pranggono

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GHMSA-Net: Gated Hierarchical Multi-Scale Self-Attention for Perceptually-Guided AV1 Post-Processing by Bopu Zhao, Woowoen Gwun, Kiho Choi

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TCSR: Lightweight Transformer and CNN Interaction Network for Image Super-Resolution by Danlin Cai, Wenwen Tan, Feiyang Chen, Xinchi Lou, Jianbin Xiahou, Daxin Zhu, Detian Huang

“…Convolutional neural network (CNN) has achieved impressive success in lightweight image super-resolution (SR) methods, yet the nature of its local operations constrains the SR performance. …”
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Simultaneous single image super‐resolution and blind Gaussian denoising via slim ghost full‐frequency residual blocks by Saghar Farhangfar, Aryaz Baradarani, Mohammad Asadpour, Mohammad Ali Balafar, Roman Gr. Maev

“…This paper presents a model for simultaneous super‐resolution and blind additive white Gaussian noise (AWGN) denoising with two components (netdeg and netSR) that is based on a generative adversarial network (GAN) to achieve detailed results. netdeg, featuring residual and innovative cost‐effective ghost residual blocks with a frequency separation module for obtaining long‐range information, blindly restores a clean version of the LR image. netSR leverages slim ghost full‐frequency residual blocks to process low‐frequency (LF) and high‐frequency (HF) information via static large convolutions and pixel‐wise highlighted input‐adaptive dynamic convolutions, respectively. …”
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RHYTHMI: A Deep Learning-Based Mobile ECG Device for Heart Disease Prediction by Alaa Eleyan, Ebrahim AlBoghbaish, Abdulwahab AlShatti, Ahmad AlSultan, Darbi AlDarbi

“…We have harnessed the power of AI, specifically deep learning and convolutional neural networks (CNNs), to develop Rhythmi, an innovative mobile ECG diagnosis device for heart disease detection. …”
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An Automatic Method for Interest Point Detection by I. G. Zubov

“…This method allows localization of interest points by analysing the inner layers of convolutional neural networks trained for the classification of images and detection of objects in an image. …”
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SEAP: squeeze-and-excitation attention guided pruning for lightweight steganalysis networks by Qiushi Li, Shenghai Luo, Shunquan Tan, Zhenjun Li

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Design and modeling of a nanocomposite system for demineralization of sweet whey by Mina Rezapour, Mohsen Esmaiili, Mehdi Mahmoudian, Alireza Behrooz Sarand

“…The dynamic flux behavior of whey output and salt rejection from whey was modeled using convolutional neural network (CNN) machine learning tools. …”
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