Fresh or Rotten? Enhancing Rotten Fruit Detection With Deep Learning and Gaussian Filtering by Leopold Fischer-Brandies, Lucas Muller, Justus Johannes Riegger, Ricardo Buettner

“…Our transfer learning-based model uses the ResNet50 convolutional neural network architecture as a binary classification model to distinguish between fresh and rotten fruits. …”
Get full text

A Novel Two-Level Protection Scheme against Hardware Trojans on a Reconfigurable CNN Accelerator by Zichu Liu, Jia Hou, Jianfei Wang, Chen Yang

“…With the boom in artificial intelligence (AI), numerous reconfigurable convolution neural network (CNN) accelerators have emerged within both industry and academia, aiming to enhance AI computing capabilities. …”
Get full text

Lightweight detection of cotton leaf diseases using StyleGAN2-ADA and decoupled focused self-attention by Henghui Mo, Linjing Wei

“…The Decoupled Focused Self-Attention (DFSA) mechanism splits traditional two-dimensional self-attention into one-dimensional operations that are processed by a dilated convolution layer, merges positional features with the original input, enhances feature relationships, and dynamically adjusts self-attention weights. …”
Get full text

Linear and Non-Linear Methods to Discriminate Cortical Parcels Based on Neurodynamics: Insights from sEEG Recordings by Karolina Armonaite, Livio Conti, Luigi Laura, Michele Primavera, Franca Tecchio

“…For this study, we used a linear Power Spectral Density (PSD) estimate and three non-linear measures: the Higuchi fractal dimension (HFD), a one-dimensional convolutional neural network (1D-CNN), and a one-shot learning model. …”
Get full text

Flowering Index Intelligent Detection of Spray Rose Cut Flowers Using an Improved YOLOv5s Model by Junyan Li, Ming Li

“…By incorporating small-scale anchor boxes and small object feature output, the model enhanced the annotation accuracy and the detection precision for occluded rose flowers. Additionally, a convolutional block attention module attention mechanism was integrated into the original network structure to improve the model’s feature extraction capability. …”
Get full text

A Review of Enhancement Techniques for Cone Beam Computed Tomography Images by Hassn Mazin Al-alaaf, Mohammed Sabah Jarjees

“…Additionally, this paper discusses the application of deep learning methods, convolutional neural networks, and generative adversarial networks in CBCT image enhancement. …”
Get full text

Land Cover Classification Model Using Multispectral Satellite Images Based on a Deep Learning Synergistic Semantic Segmentation Network by Abdorreza Alavi Gharahbagh, Vahid Hajihashemi, José J. M. Machado, João Manuel R. S. Tavares

“…In recent years, deep learning and Convolutional Neural Networks (CNNs) have significantly enhanced the segmentation of satellite images. …”
Get full text

Traffic sign detection method based on improved YOLOv8 by Gaihua Wang, Peng Jin, Zhiwei Qi, Xiaohuan Li

Get full text

Thyroid nodule classification in ultrasound imaging using deep transfer learning by Yan Xu, Mingmin Xu, Zhe Geng, Jie Liu, Bin Meng

“…Through comparative analysis, the support vector machine (SVM), which demonstrated the best diagnostic performance among traditional machine learning models, and the Inception V3 convolutional neural network model, based on transfer learning, were selected for model construction. …”
Get full text

Heterogeneous transfer learning model for improving the classification performance of fNIRS signals in motor imagery among cross-subject stroke patients by Jin Feng, YunDe Li, ZiJun Huang, Yehang Chen, SenLiang Lu, RongLiang Hu, QingHui Hu, YuYao Chen, XiMiao Wang, Yong Fan, Jing He

“…An adaptive feature matching network aligns task-relevant feature maps and convolutional layers between source (EEG) and target (fNIRS) domains. …”
Get full text

Comparative Evaluation of Traditional Methods and Deep Learning for Brain Glioma Imaging. Review Paper by Kiranmayee Janardhan, Vinay Martin D’Sa Prabhu, T. Christy Bobby

“…This review evaluates effective segmentation and classification techniques post-magnetic resonance imaging acquisition, highlighting that convolutional neural network architectures outperform traditional techniques in these tasks.…”
Get full text

Application of photon-counting CT in cardiovascular diseases by WANG Mengzhen, BAO Shouyu, LIU Peng, YAN Fuhua, YANG Wenjie

“…Although PCCT holds great potential in the diagnosis of coronary artery disease and quantitative analysis of myocardial tissues, its quantitative results remain affected by reconstruction parameters such as convolution kernels, virtual monoenergetic levels, and iterative strength. …”
Get full text

Multidimensional time series classification with multiple attention mechanism by Chen Liu, Zihan Wei, Lixin Zhou, Ying Shao

“…This paper introduces attention mechanisms applied to the temporal dimension, graph attention mechanisms for inter-dimensional relationships within multidimensional data, and attention mechanisms applied between channels post-convolutional calculations. These mechanisms are deployed for feature extraction across temporal, variational, and channel dimensions of multidimensional time series data, respectively. …”
Get full text

Music source feature extraction based on improved attention mechanism and phase feature by Weina Yu

Get full text

Efficient slice anomaly detection network for 3D brain MRI Volume. by Zeduo Zhang, Yalda Mohsenzadeh

“…Especially for 3D brain MRI data, all the state-of-the-art models are reconstruction-based with 3D convolutional neural networks which are memory-intensive, time-consuming and producing noisy outputs that require further post-processing. …”
Get full text

Optimizing Deep Learning Models for Resource‐Constrained Environments With Cluster‐Quantized Knowledge Distillation by Niaz Ashraf Khan, A. M. Saadman Rafat

“…ABSTRACT Deep convolutional neural networks (CNNs) are highly effective in computer vision tasks but remain challenging to deploy in resource‐constrained environments due to their high computational and memory requirements. …”
Get full text

Low-Power Branch CNN Hardware Accelerator with Early Exit for UAV Disaster Detection Using 16 nm CMOS Technology by Yu-Pei Liang, Wen-Chin Chao, Ching-Che Chung

“…This paper presents a disaster detection framework based on aerial imagery, utilizing a Branch Convolutional Neural Network (B-CNN) to enhance feature learning efficiency. …”
Get full text

Investigating Brain Responses to Transcutaneous Electroacupuncture Stimulation: A Deep Learning Approach by Tahereh Vasei, Harshil Gediya, Maryam Ravan, Anand Santhanakrishnan, David Mayor, Tony Steffert

“…EEGNet, a convolutional neural network specifically designed for EEG signal processing, was utilized in this work, achieving over 95% classification accuracy in detecting brain responses to various TEAS frequencies. …”
Get full text

Attention-based multimodal deep learning for interpretable and generalizable prediction of pathological complete response in breast cancer by Taishi Nishizawa, Takouhie Maldjian, Zhicheng Jiao, Tim Q. Duong

“…Methods We developed a multimodal deep learning model combining post contrast-enhanced whole-breast MRI at pre- and post-treatment timepoints with non-imaging clinical features. …”
Get full text

Human Action Recognition Based on The Skeletal Pairwise Dissimilarity by E.E. Surkov, O.S. Seredin, A.V. Kopylov

“…The paper conducts frame-by-frame annotation of activities in the TST Fall Detection v2 database, such as standing, sitting, lying, walking, falling, post-fall lying, grasp, ungrasp. A convolutional neural network based on the ResNetV2 with the SE-block is proposed to solve the activity recognition problem. …”
Get full text