AirQuaNet: A Convolutional Neural Network Model With Multi-Scale Feature Learning and Attention Mechanisms for Air Quality-Based Health Impact Prediction by Sreeni Chadalavada, Suleyman Yaman, Abdulkadir Sengur, Ravinesh C. Deo, Abdul Hafeez-Baig, Tracy Kolbe-Alexander, Niranjana Sampathila, U. Rajendra Acharya

“…The MSCBs employ four parallel 1D convolutional layers with different kernel sizes, enabling the model to extract multi-scale features critical for learning patterns in complex environmental data. …”
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RLK-YOLOv8: multi-stage detection of strawberry fruits throughout the full growth cycle in greenhouses based on large kernel convolutions and improved YOLOv8 by Lei He, Lei He, Lei He, Dasheng Wu, Dasheng Wu, Dasheng Wu, Xinyu Zheng, Xinyu Zheng, Xinyu Zheng, Fengya Xu, Fengya Xu, Fengya Xu, Shangqin Lin, Shangqin Lin, Shangqin Lin, Siyang Wang, Siyang Wang, Siyang Wang, Fuchuan Ni, Fang Zheng

“…Firstly, it utilizes the large kernel convolution network RepLKNet as the backbone to enhance the extraction of features from targets and complex backgrounds. …”
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Multi-scale conv-attention U-Net for medical image segmentation by Peng Pan, Chengxue Zhang, Jingbo Sun, Lina Guo

“…The AC module dynamically adjusts the convolutional kernel through an adaptive convolutional layer. …”
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FAULT DIAGNOSIS OF ROLLING BEARINGS BASED ON CHANNEL AND SPATIAL RECONSTRUCTION NETWORKS by ZHOU Tao, YAO DeChen, YANG JianWei

“…Since the fault vibration data collected in real engineering may be accompanied by noise，traditional diagnostic models are difficult to identify fault categories，to address this problem，a rolling bearing fault diagnosis research method based on channel and spatial reconstruction and progressive convolutional neural networks （CSRP-CNN） was proposed.The model utilizes channel and spatial reconstruction convolution （CSConv） to reduce the redundant information of channels and space in fault features，and reduces the complexity and computation to improve the performance； using convolutional block attention module （CBAM），attention enhancement operation was carried out in the channel and spatial dimensions to make the model pay attention to important fault feature information； and progressive convolutional network structure was used in the shallow layer of the network，which will fuse the previous fault feature information fused with the current input to obtain richer feature information.The performance of CSRP-CNN was evaluated by two different datasets of Case Western Reserve University（CWRU）and machinery fault simulator magnum（MFS-MG）.After the noise and ablation tests，it is verified that CSRP-CNN has strong robustness and the effects of CSConv，CBAM and progressive convolutional neural network（PCNN） on the model noise immunity performance.…”
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Fault diagnosis of rolling bearing based on channel and spatial reconstruction networks by ZHOU Tao, YAO Dechen, YANG Jianwei

“…The performance of CSRP-CNN was evaluated by two different datasets of Case Western Reserve University (CWRU) and machinery fault simulator magnum (MFS-MG). …”
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PS-YOLO: A Lighter and Faster Network for UAV Object Detection by Han Zhong, Yan Zhang, Zhiguang Shi, Yu Zhang, Liang Zhao

“…GSCD employs shared convolutions to enhance the network’s ability to learn common features across objects of different scales and introduces Normalized Gaussian Wasserstein Distance Loss (NWDLoss) to improve detection accuracy. …”
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Search for the optimal smoothing method to improve S/N in cosmic maser spectra by Egliene Anete, Aberfelds Artis

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Rolling Bearing Fault Diagnosis Based on Recurrence Plot by Zheming Chen, Bin Xu, Zhong Zhang

“…The accuracy of the method exceeds 92% on two different bearing datasets, indicating its strong generalization performance. …”
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Automated Vertebral Bone Quality Determination from T1-Weighted Lumbar Spine MRI Data Using a Hybrid Convolutional Neural Network–Transformer Neural Network by Kristian Stojšić, Dina Miletić Rigo, Slaven Jurković

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High-Precision and Low-Complexity Silicon Waveguide-Integrated Temperature Sensor System by Zhiming Zhang, Haole Kong, Yi Li

“…The waveguide layout is optimized through the finite-difference time-domain (FDTD) simulations, and a compressed taper structure improves the efficiency of speckle data collection while reducing the system complexity and cost. …”
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Novel multistage deep convolution neural network-based Parkinson’s disease detection and severity grading of running speech using LSF spectrums for detection and STFT spectrums for... by Rani Kumari, Prakash Ramachandran

“…The dataset is arranged in 10 different combinations that includes 4 binary classification detection problem and 6 multiclass classification severity grading problem and CNN learning experiments are conducted using LSF, STFT and Mel frequency cepstral coefficients (MFCC) spectrums. …”
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Reconstruction of concrete morphology using deep learning by Ondřej Šperl, Jan Sýkora

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A novel chaotic interleaving algorithm for mobile wireless channels by Xianping WANG, Hui CAO

“…Interleaving technique is an efficient technique to resist serious burst errors over mobile wireless fading channels.To resist 2 dimensionality burst errors effectively,a novel chaotic interleaving algorithm based on Baker map was proposed.In the proposed scheme,the binary source sequence was converted to the data matrix,and then the data matrix was dispersed randomly by using the chaotic Baker map approach,in order to realize the function of transforming 2 dimensionality long bust error into the short 1 dimensionality short bust error after de-interleaving.In additional,the proposed algorithm was combined with the convolution code based on Viterbi decoding,and was applied into the scenario of convolutional codes (2,1,3) and the scenario of (2,1,7) separately for a performance comparison.The simulation results show that the performance of the proposed algorithm outperforms better than the traditional algorithms under image transmission over mobile wireless channel.Moreover,the anti-fading capability of the proposed algorithm grows as the packet length increases,while reducing the complexity significantly.Finally,the chaotic interleaver can also enhance every transmitted packet's security with different secret keys.…”
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Custom YOLO Object Detection Model for COVID-19 Diagnosis by Noor Najah Ali, Aseel Hameed, Asanka G. Perera, Ali Al_Naji

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Advanced Diabetic Retinopathy Detection with the R–CNN: A Unified Visual Health Solution by Sravya Valluri Sri, Srinivasu Parvathaneni Naga, Shafi Jana, Hołubowski Waldemar, Zielonka Adam

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Classifying breast intraductal proliferative lesions via a knowledge distillation framework using convolutional neural network-based nuclei-segmentation-assisted classification (KD... by Xiangmin Li, Jiamei Chen, Bo Luo, Minyan Xia, Xu Zhang, Hangjia Zhu, Yutian Zhang-Cai, Yongshun Chen, Yang Yang, Yaofeng Wen

“…Background and objective: Diagnosis of breast intraductal proliferative lesions (BIDPLs) in hematoxylin-eosin (HE) images remains a time-consuming and intractable topic because of subjective processes and subtle morphological differences. Convolutional neural networks (CNNs) show great potential for providing objective analysis strategies for HE images. …”
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Solar Radio Burst Detection Based on Deformable DETR by Mingming Wang, Guowu Yuan, Hao Zhou, Chengming Tan, Hao Wu

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ACM-YOLOv10: Research on Classroom Learning Behavior Recognition Algorithm Based on Improved YOLOv10 by Beichen Qin, Haoyan Hu, Shaowen Du

“…The model is designed with an Asymmetric Depthwise Separable Convolution (ADSConv) module to replace the traditional convolutional layers. …”
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Joint classification and regression with deep multi task learning model using conventional based patch extraction for brain disease diagnosis by Padmapriya K., Ezhumalai Periyathambi

“…The proposed model learns many tasks concurrently, such as categorizing different brain diseases or anomalies, by extracting features from image patches using convolutional neural networks (CNNs). …”
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Comparative study of citrus fruits (Citrus reticulata Blanco cv. Batu 55) detection and counting with single and double labels based on convolutional neural network using YOLOv7 by Dimas Firmanda Al Riza, Lucky Candra Musahada, Romzi Izzudin Aufa, Mochamad Bagus Hermanto, Hermawan Nugroho, Yusuf Hendrawan

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