Duck Egg Crack Detection Using an Adaptive CNN Ensemble with Multi-Light Channels and Image Processing by Vasutorn Chaowalittawin, Woranidtha Krungseanmuang, Posathip Sathaporn, Boonchana Purahong

“…Therefore, this paper presents duck egg crack detection using an adaptive convolutional neural network (CNN) model ensemble with multi-light channels. …”
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A multi-scale cross-dimension interaction approach with adaptive dilated TCN for RUL prediction by Zhe Lu, Bing Li, Changyu Fu, Liang Xu, Bai Jiang, Zelong Li, Junbao Wu, Siye Jia

“…First, a dynamic adaptive dilation factor is incorporated into the TCN, thereby enabling the model to adjust its receptive field dynamically, which facilitates the capture of long- and short-term dependencies across different scales, allowing a more comprehensive representation of equipment degradation patterns. …”
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DermViT: Diagnosis-Guided Vision Transformer for Robust and Efficient Skin Lesion Classification by Xuejun Zhang, Yehui Liu, Ganxin Ouyang, Wenkang Chen, Aobo Xu, Takeshi Hara, Xiangrong Zhou, Dongbo Wu

“…Dermoscopic Feature Gate (DFG), which simulates the observation–verification operation of doctors through a convolutional gating mechanism and effectively suppresses semantic leakage of artifact regions. …”
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BCTDNet: Building Change-Type Detection Networks with the Segment Anything Model in Remote Sensing Images by Wei Zhang, Jinsong Li, Shuaipeng Wang, Jianhua Wan

“…Subsequently, an attribute-aware strategy is adopted to explicitly generate distinct maps for newly constructed and demolished buildings, thereby establishing clear temporal relationships among different change types. To evaluate BCTDNet’s performance, we construct the JINAN-MCD dataset, which covers Jinan’s urban core area over a six-year period, capturing diverse change scenarios. …”
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A Comprehensive Evaluation of Monocular Depth Estimation Methods in Low-Altitude Forest Environment by Jiwen Jia, Junhua Kang, Lin Chen, Xiang Gao, Borui Zhang, Guijun Yang

“…The evaluated models include both self-supervised and supervised approaches, employing different network structures such as convolutional neural networks (CNNs) and Vision Transformers (ViTs). …”
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RETRACTED ARTICLE: A combined microfluidic deep learning approach for lung cancer cell high throughput screening toward automatic cancer screening applications by Hadi Hashemzadeh, Seyedehsamaneh Shojaeilangari, Abdollah Allahverdi, Mario Rothbauer, Peter Ertl, Hossein Naderi-Manesh

“…We designed and tested a deep learning image analysis workflow for classification of lung cancer cell-line images into six classes, including five different cancer cell-lines (P-C9, SK-LU-1, H-1975, A-427, and A-549) and normal cell-line (16-HBE). …”
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An automated hip fracture detection, classification system on pelvic radiographs and comparison with 35 clinicians by Abdurrahim Yilmaz, Kadir Gem, Mucahit Kalebasi, Rahmetullah Varol, Zuhtu Oner Gencoglan, Yegor Samoylenko, Hakan Koray Tosyali, Guvenir Okcu, Huseyin Uvet

“…The YOLOv5 architecture was employed for the object detection model, while three different pre-trained deep neural network (DNN) architectures were used for classification, applying transfer learning. …”
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Do more with less: Exploring semi-supervised learning for geological image classification by Hisham I. Mamode, Gary J. Hampson, Cédric M. John

“…Overall, SSL is a promising approach and future work should explore this approach utilizing different dataset types, quantity, and quality.…”
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A hybrid hierarchical health monitoring solution for autonomous detection, localization and quantification of damage in composite wind turbine blades for tinyML applications by Nikhil Holsamudrkar, Shirsendu Sikdar, Akshay Prakash Kalgutkar, Sauvik Banerjee, Rakesh Mishra

“…This paper presents a Hybrid Hierarchical Machine-Learning Model (HHMLM) that leverages acoustic emission (AE) data to identify, classify, and locate different types of damage using the single unified model. …”
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Lightweight CNN model for automatic detection and depth estimation of subsurface voids using GPR B-scan data by Abdelaziz Mojahid, Driss EL Ouai, Khalid EL Amraoui, Khalil EL-Hami, Hamou Aitbenamer, Jochem Verrelst, Pier Matteo Barone

“…The model was trained on 1408 augmented B-scans collected with 200 and 400 ​MHz antennas across various subsurface materials, ensuring exposure to a wide range of material types with different electromagnetic properties. Testing experiments were performed using eight profiles where cavity detection was confirmed by borehole data. …”
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Unified estimation of rice canopy leaf area index over multiple periods based on UAV multispectral imagery and deep learning by Haixia Li, Qian Li, Chunlai Yu, Shanjun Luo

“…Results In this study, a multispectral camera mounted on a UAV was utilized to acquire rice canopy image data, and rice LAI was uniformly estimated over multiple periods by the multilayer perceptron (MLP) and convolutional neural network (CNN) models in deep learning. …”
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A Deep Learning-Based Approach for Cell Segmentation in Phase-Contrast Images by Basma A. Mohamed, Nancy M. Salem, Walid Al-Atabany, Lamees N. Mahmoud

“…The findings highlight the potential of Ranger and the generalized training model to enhance cell segmentation across different microscopy datasets.…”
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SVD-Based Feature Reconstruction Metric Network With Active Contrast Loss for Few-Shot SAR Target Recognition by Jia Zheng, Ming Li, Xiang Li, Peng Zhang, Yan Wu

“…Synthetic aperture radar (SAR) automatic target recognition (ATR) methods based on convolutional neural networks require a large number of samples to achieve good generalize. …”
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SGSNet: a lightweight deep learning model for strawberry growth stage detection by Zhiyu Li, Jianping Wang, Guohong Gao, Yufeng Lei, Chenping Zhao, Yan Wang, Haofan Bai, Yuqing Liu, Xiaojuan Guo, Qian Li

“…The DySample adaptive upsampling structure is employed to dynamically adjust sampling point locations, thereby enhancing the detection capability for objects at different scales. The RepNCSPELAN4 module is optimized with the iRMB lightweight attention mechanism to achieve efficient multi-scale feature fusion, significantly improving the accuracy of detecting small targets from long-distance images. …”
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Effective data selection via deep learning processes and corresponding learning strategies in ultrasound image classification by Hyunju Lee, Jin Young Kwak, Eunjung Lee

“…Additionally, the True network showed strong performance when applied to the Vision Transformer and similar enhancements were observed across multiple convolutional neural network architectures. Furthermore, to assess the robustness and adaptability of our method across different medical imaging modalities, we applied it to dermoscopic images and observed similar performance enhancements. …”
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Img2Neuro: brain-trained neural activity encoders for enhanced object recognition by Mona A Aboelnaga, Mohamed W El-Kharashi, Seif Eldawlatly

“…In our experiments, we examined the classification performance when Img2Neuro is used as a feature extractor compared to using the images as direct input to the classifier, using five different classifiers; namely, linear discriminant analysis, perceptron, logistic regression, ridge classifier, and a single-layer neural network. …”
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Improving drug-induced liver injury prediction using graph neural networks with augmented graph features from molecular optimisation by Taeyeub Lee, Joram M. Posma

“…Furthermore, DILIGeNN outperformed the state-of-the-art in other graph-based molecular prediction tasks, achieving an AUC of 0.918 on the Clintox dataset, 0.993 on the BBBP dataset, and 0.953 on the BACE dataset, indicating strong generalisation and performance across different datasets. Conclusion DILIGeNN, utilising a single graph representation as input, outperforms the state-of-the-art methods in DILI prediction that incorporate both molecular fingerprint and graph-structured data. …”
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Hyperspectral Detection of Pesticide Residues in Black Vegetable Based on Multi-Classifier Entropy Weight Method by Rongchang Jiang, Guoqiang Zhuang, Shijie Xie, Yang Wang, Guoqi Zhang, Dandan Qu, Wanzhi Wen

“…Bailey) by proposing a multi-classifier entropy weighted method algorithm that combines hyperspectral technology and the entropy weight method. 10 black vegetable samples were sprayed with each of the four different pesticides (trichlorfon, propargite, cypermethrin, and imidacloprid) at concentrations of 0.10, 2.00, 0.20, and 2.00 mg/kg, respectively. …”
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Contextual Deep Semantic Feature Driven Multi-Types Network Intrusion Detection System for IoT-Edge Networks by Shaho Hassen, Ahmed Abdlrazaq

“… Recent years have witnessed an exponential rise in wireless networks and allied interoperable distributed computing frameworks, where the different sensory units transfer real-world event data to the network analyzer for run-time decisions. …”
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Edge-based detection and localization of adversarial oscillatory load attacks orchestrated by compromised EV charging stations by Khaled Sarieddine, Mohammad Ali Sayed, Sadegh Torabi, Ribal Atallah, Chadi Assi

“…Moreover, this analysis results shed light on the impact of such detection mechanisms towards building resiliency into different levels of the EV charging ecosystem while allowing power grid operators to localize attacks and take further mitigation measures. …”
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