YOLOv8-OCHD: A Lightweight Wood Surface Defect Detection Method Based on Improved YOLOv8 by Zuxing Chen, Junjie Feng, Xueyan Zhu, Bin Wang

“…This effectively reduces the difficulty and cost of deployment on mobile terminals while significantly improving algorithm accuracy, meeting real-time requirements, and providing a more efficient and feasible technical solution for relevant applications. …”
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RSM-YOLOv11: Lightweight Steel Surface Defect Segmentation Algorithm Research Based on YOLOv11 Improvement by Zenghai Shan, Hu Haoyan, Changjian Zhu, Shaowen Du, Hongtao Jing, Wang Haibin

“…The Space-to-Depth Convolution (SPD-Conv) module is introduced to replace the traditional convolutional layer. …”
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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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Real-Time Defect Detection for Fast-Moving Fabrics on Circular Knitting Machine Under Various Illumination Conditions by Yan-Qin Ni, Pei-Kai Huang, Ching-Han Yang, Chin-Chun Chang, Wei-Jen Wang, Deron Liang

“…Next, due to practical constraints aimed at maintaining high yield rates, collecting sufficient abnormal fabric samples for model training is costly and limited. Furthermore, circular knitting machines typically operate under varying illumination conditions, further complicating the task of accurate fabric defect detection. …”
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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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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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METHOD FOR EVALUATING EXCAVATION SHORING DESIGN CONCEPTS by Yu. G. Zheglova, B. P. Titarenko

“…The study is based on dynamic programming methods based on the principle of Bellman optimality. Logical convolution matrices are applied according to the dichotomy method.Results. …”
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PM2.5 prediction using population-based centrality weight by Hee Joon Choi, Won Kyung Lee, So Young Sohn

“…We propose to apply a population-based centrality weight to the cost function of the forecasting model, reflecting both of residential and changes in active populations. …”
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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

“…These results demonstrate the NDVI prediction method’s potential for cost-effective, real-time plant health assessment, particularly in agrobotics.…”
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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

“…The developed mobile ECG diagnosis device addresses the main problems of traditional ECG diagnostic devices such as accessibility, cost, mobility, complexity, and data integration. …”
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An Automatic Method for Interest Point Detection by I. G. Zubov

“…The proposed method allows identification of interest points without incurring additional costs of data annotation and training.Results. The conducted experiments confirmed the correctness of the proposed method in identifying interest points. …”
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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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