Proposing a Fuzzy Soft‐max‐based classifier in a hybrid deep learning architecture for human activity recognition by Reza Shakerian, Meisam Yadollahzadeh‐Tabari, Seyed Yaser Bozorgi Rad

“…The authors were also interested in considering a post‐processing module that considers activity classification over a longer period. …”
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Fresh or Rotten? Enhancing Rotten Fruit Detection With Deep Learning and Gaussian Filtering by Leopold Fischer-Brandies, Lucas Muller, Justus Johannes Riegger, Ricardo Buettner

“…More than half of the fruit yield is lost along the supply chain, with post-harvest losses due to rottenness playing a pivotal role, as even a single decomposing piece can cause huge damage to nearby produce. …”
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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

“…Here, we explore linear and non-linear methods using data from a public stereotactic intracranial EEG (sEEG) dataset, focusing on the superior temporal gyrus (STG), postcentral gyrus (postCG), and precentral gyrus (preCG) in 55 subjects during resting-state wakefulness. …”
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Investigating Brain Responses to Transcutaneous Electroacupuncture Stimulation: A Deep Learning Approach by Tahereh Vasei, Harshil Gediya, Maryam Ravan, Anand Santhanakrishnan, David Mayor, Tony Steffert

“…Additionally, the classification accuracies across the pre-stimulation, during-stimulation, and post-stimulation phases remained consistently high (above 92%), indicating that EEGNet effectively captured the different time-based brain responses across different stimulation phases. …”
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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. …”
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Comparison between coronal FLASH and sagittal double echo steady state MRI in detecting longitudinal cartilage thickness change by fully automated segmentation – Data from the FNIH... by Felix Eckstein, Akshay S. Chaudhari, David J. Hunter, Wolfgang Wirth

“…Post-processing involved automated registration of CNN-based subchondral bone segmentation to reference areas. …”
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Optimal Res-UNET architecture with deep supervision for tumor segmentation by Rahman Maqsood, Fazeel Abid, Jawad Rasheed, Jawad Rasheed, Jawad Rasheed, Onur Osman, Shtwai Alsubai

“…The proposed network was evaluated using extensive ablation studies, examining the effects of encoder complexity, convolutional filter count, and strategic post-processing.ResultsThe proposed Res-UNET with deep supervision outperformed other variants, achieving an average Dice score of 0.9498 through five-fold cross-validation. …”
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Deep learning-enhanced anti-noise triboelectric acoustic sensor for human-machine collaboration in noisy environments by Chuanjie Yao, Suhang Liu, Zhengjie Liu, Shuang Huang, Tiancheng Sun, Mengyi He, Gemin Xiao, Han Ouyang, Yu Tao, Yancong Qiao, Mingqiang Li, Zhou Li, Peng Shi, Hui-jiuan Chen, Xi Xie

“…Abstract Human-machine voice interaction based on speech recognition offers an intuitive, efficient, and user-friendly interface, attracting wide attention in applications such as health monitoring, post-disaster rescue, and intelligent control. However, conventional microphone-based systems remain challenging for complex human-machine collaboration in noisy environments. …”
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CSDNet: Context-Aware Segmentation of Disaster Aerial Imagery Using Detection-Guided Features and Lightweight Transformers by Ahcene Zetout, Mohand Saïd Allili

“…The architecture combines a lightweight transformer module for global context modeling with depthwise separable convolutions (DWSCs) to enhance efficiency without compromising representational capacity. …”
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Flowering Index Intelligent Detection of Spray Rose Cut Flowers Using an Improved YOLOv5s Model by Junyan Li, Ming Li

“…The WIoU loss function was employed in place of the original CIoU loss function to increase the precision of the model’s post-detection processing. Test results indicated that for two types of spray rose cut flowers, Orange Bubbles and Yellow Bubbles, the improved YOLOv5s model achieved an accuracy and recall improvement of 10.2% and 20.0%, respectively. …”
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A Review of Enhancement Techniques for Cone Beam Computed Tomography Images by Hassn Mazin Al-alaaf, Mohammed Sabah Jarjees

“…The review covers a range of preprocessing and post-processing methods, including denoising, artifact correction, and resolution improvement techniques. …”
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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.…”
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Historicizing National Socialism and Mehmet Genç by Ahmet Okumuş

“…The depiction of the Nazi era in post-war historiography emerged as a contentious realm of debate. …”
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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. …”
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Music source feature extraction based on improved attention mechanism and phase feature by Weina Yu

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

“…Conventional model compression techniques, such as pruning and post‐training quantization, often compromise model accuracy by decoupling compression from training. …”
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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. …”
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Enhancing CNN-Based Signal Denoising: A Novel Metric Framework With Harmonic Suppression Through Hybrid Modeling by Omer Nacar, Turgay Koc

“…Convolutional neural networks (CNNs) show promise for signal denoising but can introduce harmonic distortions due to their nonlinearity. …”
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A Comprehensive Review of Deep Learning in Computer Vision for Monitoring Apple Tree Growth and Fruit Production by Meng Lv, Yi-Xiao Xu, Yu-Hang Miao, Wen-Hao Su

“…Three types of deep learning models were used for real-time target recognition tasks: detection models including You Only Look Once (YOLO) and faster region-based convolutional network (Faster R-CNN); classification models including Alex network (AlexNet) and residual network (ResNet); segmentation models including segmentation network (SegNet), and mask regional convolutional neural network (Mask R-CNN). …”
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