Automated Detection of Kaposi Sarcoma–Associated Herpesvirus–Infected Cells in Immunohistochemical Images of Skin Biopsies by Iftak Hussain, Juan Boza, Robert Lukande, Racheal Ayanga, Aggrey Semeere, Ethel Cesarman, Jeffrey Martin, Toby Maurer, David Erickson

“…This work aims to develop an automated framework for localization and quantification of LANA positivity in whole-slide images (WSI) of skin biopsies.METHODSThe proposed framework leverages weakly supervised multiple-instance learning (MIL) to reduce false-positive predictions. …”
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Interpretable Ordinal-Aware With Contrastive-Enhanced Anomaly Severity Detection on UAV Flight Log Messages by Swardiantara Silalahi, Tohari Ahmad, Hudan Studiawan, Eirini Anthi, Lowri Williams

“…Detecting abnormalities in a system using log records is a common approach, although such applications may vary between different deployment domains. …”
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The emerging role of blood-based biomarkers in early detection of colorectal cancer: A systematic review by Faris Shweikeh, Yuhao Zeng, Abdur Rahman Jabir, Erica Whittenberger, Saurav P. Kadatane, Yuting Huang, Mohamad Mouchli, Dani Ran Castillo

“…Background: Colorectal cancer (CRC), the third most commonly diagnosed and second most lethal cancer worldwide, necessitates efficient early detection strategies to improve patient outcomes. This review evaluates the promise of novel blood-based biomarkers for early detection of CRC. …”
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Adapting Vision Transformer-Based Object Detection Model for Handwritten Text Line Segmentation Task by Osman Furkan KARAKUŞ, Ayla GULCU, Ali Can KARACA

“…Specifically, we adapt DEtection TRansformer (DETR) model to detect line segments in images of handwritten documents. …”
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Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data by T. Radke, S. Fuchs, C. Wilms, I. Polkova, I. Polkova, I. Polkova, M. Rautenhaus, M. Rautenhaus

“…<p>Detection of atmospheric features in gridded datasets from numerical simulation models is typically done by means of rule-based algorithms. …”
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Artificial Intelligence Driven Smart Farming for Accurate Detection of Potato Diseases: A Systematic Review by Avneet Kaur, Gurjit S. Randhawa, Farhat Abbas, Mumtaz Ali, Travis J. Esau, Aitazaz A. Farooque, Rajandeep Singh

“…However, AI tools, for instance, Machine Learning (ML) and Deep Learning (DL), offer precise and well-timed solutions for disease detection, classification, and eradication. …”
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Non-invasive detection of pesticide residues in freshly harvested olives using hyperspectral imaging technology by Diego Manuel Martínez Gila, David Bonillo Martínez, Silvia Satorres Martínez, Pablo Cano Marchal, Javier Gámez García

“…This article proposes a methodology based on hyperspectral imaging to detect whether the olives have been sprayed with pesticide products and, if so, when the spraying occurred. …”
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Ensemble deep learning and anomaly detection framework for automatic audio classification: Insights into deer vocalizations by Salem Ibrahim Salem, Sakae Shirayama, Sho Shimazaki, Kazuo Oki

“…This segmentation method relies on acoustic anomaly detection and can markedly improve computational efficiency, facilitating deployment in environments with limited resources. …”
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Observational Constraint on the Contributions of Greenhouse Gas Emission and Anthropogenic Aerosol Removal to Tibetan Plateau Future Warming by Jie Jiang, Tianjun Zhou

“…Taking the Tibetan Plateau (TP) as an instance, analyses of the state‐of‐the‐art climate models participating in the Detection and Attribution Model Intercomparison Project imply that while the observed warming from 1961 to 2020 is predominantly attributed to GHG emission, the future temperature rise will be influenced by the combined effects of persistent increase in GHG concentration and reduction of AA emission. …”
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Machine Learning Based Method for Insurance Fraud Detection on Class Imbalance Datasets With Missing Values by Ahmed A. Khalil, Zaiming Liu, Ahmed Fathalla, Ahmed Ali, Ahmad Salah

“…The fraud datasets are usually imbalanced, as the number of fraudulent instances is much less than the ligament instances and contains missing values. …”
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Optimizing IoT intrusion detection with cosine similarity based dataset balancing and hybrid deep learning by Arvind Prasad, Wael Mohammad Alenazy, Naved Ahmad, Gauhar Ali, Hanaa A. Abdallah, Sadique Ahmad

“…However, a significant challenge IDS faces is dealing with imbalanced datasets, where attack instances are significantly underrepresented compared to normal traffic. …”
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The TouCAN Codebook: Detecting textual misunderstanding in doctor-patient communication with the philosophy of language tools. by Monica Consolandi, Mara Floris, Cristina Ganz, Noemi Paciscopi

“…This paper introduces a novel approach to detecting and analyzing such misunderstandings in clinical interactions by drawing on concepts from the philosophy of language. …”
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Evaluation of Single-Shot Object Detection Models for Identifying Fanning Behavior in Honeybees at the Hive Entrance by Tomyslav Sledevič

“…This study presents a novel dataset of 18,000 annotated video frames containing 57,597 instances capturing fanning behavior at the hive entrance across diverse conditions. …”
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A relation-enhanced mean-teacher framework for source-free domain adaptation of object detection by Dingqing Tian, Changbo Xu, Shaozhong Cao

“…Source-Free Domain Adaptation Object Detection (SF-DAOD) is a challenging task in the field of computer vision. …”
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Oak-YOLO: A high-performance detection model for automated Oak seed defect identification. by Hao Li, Zhuqi Li, Dongkui Chen, Wangyu Wu, Xuanlong He, Hongbo Mu

“…To address this challenge, we developed a high-resolution imaging system and proposed an improved YOLO-based model named Oak-YOLO for efficient and accurate defect detection in oak seeds. The proposed model enhances the YOLOv8 architecture by incorporating EfficientViT as the backbone to improve global feature extraction, and integrates a Ghost-DynamicConv detection head to enhance the representation of small and irregular defects such as insect holes and cracks. …”
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Enhancing Hazard Detection and Risk Severity Assessment in Construction through Multinomial Naive Bayes and Regression by Akaninyene Michael Akwaisua, Anietie Ekong, Godwin Ansa

“…This research delves into the crucial area of hazard detection and risk severity assessment within the construction industry, using machine learning techniques. …”
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SH17: A dataset for human safety and personal protective equipment detection in manufacturing industry by Hafiz Mughees Ahmad, Afshin Rahimi

“…We focus on developing non-invasive techniques based on the Object Detection (OD) and Convolutional Neural Network (CNN). …”
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A Comparative Analysis of YOLOv9, YOLOv10, YOLOv11 for Smoke and Fire Detection by Eman H. Alkhammash

“…Forest fires cause extensive environmental damage, making early detection crucial for protecting both nature and communities. …”
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Enhancing Rare Class Performance in HOI Detection with Re-Splitting and a Fair Test Dataset by Gyubin Park, Afaque Manzoor Soomro

“…In Human–Object Interaction (HOI) detection, class imbalance severely limits the performance of a model on infrequent interaction categories. …”
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Cyberbullying detection of resource constrained language from social media using transformer-based approach by Syed Sihab-Us-Sakib, Md. Rashadur Rahman, Md. Shafiul Alam Forhad, Md. Atiq Aziz

“…To combat this issue, we have developed the Cyberbullying Bengali Dataset (CBD), a novel resource containing 2751 manually labeled texts categorized into five classes, including various forms of cyberbullying and non-bullying instances. In our study on cyberbullying detection, we conducted an extensive evaluation of various machine learning and deep learning models. …”
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