Pesticide Residue Coverage Estimation on Citrus Leaf Using Image Analysis Assisted by Machine Learning by Adarsh Basavaraju, Edwin Davidson, Giulio Diracca, Chen Chen, Swadeshmukul Santra

“…In addition, Kocide 3000 and Oxytetracycline (OTC) residue coverage analysis was carried out using a specialized Mask Region-Based Convolutional Neural Network (CNN). …”
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Study on the quantitative analysis of Tilianin based on Raman spectroscopy combined with deep learning. by Wen Jiang, Wei Liu, Xiaotong Xin, Wei Zhang, Junhui Chen, Jieyu Liu, Yanqi Ma, Cheng Chen, Xiaomei Pan

“…High performance liquid chromatography (HPLC) and mass spectrometry (MS) are commonly used methods for the quantitative analysis of the components, but they often require complex pretreatment steps and specialized analytical capabilities, and are sample-destructive. …”
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A Study on an Improved Generative Adversarial Network Model for Super-Resolution Image Reconstruction by Ximin Qu, Cheng Peng

“…Future work will explore more efficient optimization methods and lightweight network architectures to facilitate the application of image super-resolution systems in mobile platforms and specialized fields like remote sensing and medical imaging.…”
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A Deep-Learning Approach to Heart Sound Classification Based on Combined Time-Frequency Representations by Leonel Orozco-Reyes, Miguel A. Alonso-Arévalo, Eloísa García-Canseco, Roilhi F. Ibarra-Hernández, Roberto Conte-Galván

“…These images are used to train five convolutional neural networks (CNNs): AlexNet, VGG-16, ResNet50, a CNN specialized in STFT images, and our proposed CNN model. …”
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A Novel Audio Copy Move Forgery Detection Method With Classification of Graph-Based Representations by Beste Ustubioglu, Gul Tahaoglu, Arda Ustubioglu, Guzin Ulutas, Muhammed Kilic

“…Graph coloring algorithms are applied to convert the graph into a visual representation, which is then input into a specially designed Convolutional Neural Network (CNN) model for classification. …”
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YOLOrot2.0: A novel algorithm for high-precision rice seed size measurement with real-time processing by Jinfeng Zhao, Zeyu Hou, Qin Wang, Sheng Dai, Kaicheng Yong, Xuan Wang, Jiawen Yang, Qianlong Nie, Yan Ma, Xuehui Huang

“…The current methods for rice seed measurement exhibit various limitations, such as the need for seed preprocessing and selection, specialized equipment for measurement, and prolonged measurement time. …”
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Crack-Based Estimation of Seismic Damage Level in Confined Masonry Walls in the Lima Metropolitan Area Using Deep Learning Techniques by Miguel Diaz, Luis Lopez, Michel Amancio, Italo Inocente, Jhianpiere Salinas, Sergio Isuhuaylas, Erika Flores, Edisson Moscoso

“…Contact methods, like physical measurements, material sampling, and ultrasonic testing, provide detailed data but are time-consuming and require specialized equipment. In contrast, non-contact methods assess damage remotely, allowing for faster, safer, and large-scale evaluations, especially useful in post-disaster scenarios. …”
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Revolutionizing Mental Health Sentiment Analysis With BERT-Fuse: A Hybrid Deep Learning Model by Md. Mithun Hossain, Sanjara, Md. Shakil Hossain, Sudipto Chaki, Md. Saifur Rahman, A. B. M. Shawkat Ali

“…Traditional methods are limited by the complexity of mental health-related texts, which contain specialized terminology and domain-specific nuances. …”
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Multimodal Fusion Mamba Network for Joint Land Cover Classification Using Hyperspectral and LiDAR Data by Haizhu Pan, Ruixiang Zhao, Haimiao Ge, Moqi Liu, Quanxiu Zhang

“…The elevation-enhanced Mamba, designed for LiDAR data, utilizes a specialized scanning method to extract elevation features. …”
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Rancang Bangun Aplikasi Berbasis Android untuk Perbaikan Kualitas Citra Tanaman Atas Perbedaan Spesifikasi Kamera Smartphone pada Prediksi Kandungan Pigmen Fotosintesis Secara Real... by Felix Adrian Tjokro Atmodjo, Kestrilia Rega Prilianti, Hendry Setiawan

“…The destructive approach of evaluating pigment content is expensive and necessitates specialized handling. An Android based application called Fuzzy Piction could predict the content of those pigments nondestructively using the FP3Net, a Convolutional Neural Network (CNN) model. …”
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RT-DETR-Smoke: A Real-Time Transformer for Forest Smoke Detection by Zhong Wang, Lanfang Lei, Tong Li, Xian Zu, Peibei Shi

“…To tackle these issues, we propose RT-DETR-Smoke, a specialized real-time transformer-based smoke-detection framework. …”
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A User-Friendly Machine Learning Pipeline for Automated Leaf Segmentation in by Michelle Lynn Yung, Kamila Murawska-Wlodarczyk, Alicja Babst-Kostecka, Raina Margaret Maier, Nirav Merchant, Aikseng Ooi

“…Our findings highlight that transfer learning on a specialized data set can still outperform a large foundation model in domain-specific tasks. …”
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A web-based artificial intelligence system for label-free virus classification and detection of cytopathic effects by Zeynep Akkutay-Yoldar, Mehmet Türkay Yoldar, Yiğit Burak Akkaş, Sibel Şurak, Furkan Garip, Oğuzcan Turan, Bengisu Ekizoğlu, Osman Can Yüca, Aykut Özkul, Barış Ünver

“…Abstract Identifying viral replication within cells demands labor-intensive isolation methods, requiring specialized personnel and additional confirmatory tests. …”
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Cytopathological quantification of NORs using artificial intelligence to oral cancer screening by Tatiana Wannmacher LEPPER, Luara Nascimento do AMARAL, Ana Laura Ferrares ESPINOSA, Igor Cavalcante GUEDES, Maikel Maciel RÖNNAU, Natália Batista DAROIT, Alex Nogueira HAAS, Fernanda VISIOLI, Manuel Menezes de OLIVEIRA NETO, Pantelis Varvaki RADOS

“…The present study aimed to define argyrophilic proteins of the nucleolar organizer region (AgNOR) cut-off risk points by oral exfoliative cytological smears comparing specialized humans with a convolutional neural network (CNN) system AgNOR Slide-Image Examiner. …”
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A New Transformer Network for Short-Term Global Sea Surface Temperature Forecasting: Importance of Eddies by Tao Zhang, Pengfei Lin, Hailong Liu, Pengfei Wang, Ya Wang, Weipeng Zheng, Zipeng Yu, Jinrong Jiang, Yiwen Li, Hailun He

“…This study introduces a specialized Transformer model (U-Transformer) to forecast global short-term SST variability and compares its performance with Convolutional Long Short-Term Memory (ConvLSTM) and Residual Neural Network (ResNet) models. …”
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A spatiotemporal model for urban taxi Origin–Destination prediction based on Multi-hop GCN and Hierarchical LSTM by Jiang Rong, Wangtu Xu, Yanjie Wen

“…To this end, we propose a Dual-Branch Spatial–Temporal Network (DBSTNet) for forecasting urban taxi OD demand. Specially, DBSTNet models pick-up and drop-off patterns via transposed OD matrices and employs 2 independent branches to separately capture spatial–temporal features. …”
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Advances in Remote Sensing and Deep Learning in Coastal Boundary Extraction for Erosion Monitoring by Marc-André Blais, Moulay A. Akhloufi

“…The presented algorithms range from basic convolutional networks to encoder–decoder architectures and attention mechanisms. …”
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Challenges in AI-driven multi-omics data analysis for Oncology: Addressing dimensionality, sparsity, transparency and ethical considerations by Maryem Ouhmouk, Shakuntala Baichoo, Mounia Abik

“…In this review, we gathered published methods of deep learning-based multi-omics integration specialized in oncology since 2020. We concentrated exclusively on studies utilizing cancer omics data mainly sourced from The Cancer Genome Atlas (TCGA) database. …”
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A Deep Learning-Based Diagnostic Framework for Shaft Earthing Brush Faults in Large Turbine Generators by Katudi Oupa Mailula, Akshay Kumar Saha

“…The proposed framework combines advanced signal processing and convolutional neural networks (CNNs) to automatically recognize fault-related patterns in shaft grounding current and voltage signals. …”
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An advanced deep learning method for pepper diseases and pests detection by Xuewei Wang, Jun Liu, Qian Chen

“…Built upon YOLOv10n, YOLO-Pepper incorporates four major innovations: (1) an Adaptive Multi-Scale Feature Extraction (AMSFE) module that improves feature capture through multi-branch convolutions; (2) a Dynamic Feature Pyramid Network (DFPN) enabling context-aware feature fusion; (3) a specialized Small Detection Head (SDH) tailored for minute targets; and (4) an Inner-CIoU loss function that enhances localization accuracy by 18% compared to standard CIoU. …”
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