Aircraft Skin Machine Learning-Based Defect Detection and Size Estimation in Visual Inspections by Angelos Plastropoulos, Kostas Bardis, George Yazigi, Nicolas P. Avdelidis, Mark Droznika

“…The models were optimized for detecting various aircraft skin defects, with a focus on the challenging task of dent detection. …”
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Cross-device federated unsupervised learning for the detection of anomalies in single-lead electrocardiogram signals. by Maximilian Kapsecker, Stephan M Jonas

“…The autoencoder was trained collaboratively using federated learning with twenty mobile devices, followed by an additional ten epochs of on-device fine-tuning to account for personalization.…”
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Advanced Deep Learning Models for Melanoma Diagnosis in Computer-Aided Skin Cancer Detection by Ranpreet Kaur, Hamid GholamHosseini, Maria Lindén

“…Therefore, an automated model could be developed that assists with early skin cancer detection. It is possible to limit the severity of melanoma by detecting it early and treating it promptly. …”
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Federated Learning for Distributed IoT Security: A Privacy-Preserving Approach to Intrusion Detection by Chandu Gutti, Karthik Thumula, Parag Balbudhe

“…Billions of resource-constrained IoT devices now stream security-critical yet privacy-sensitive traffic across the Internet. To detect intrusions without exposing raw data, we propose a federated learning (FL) framework that trains collaboratively on the edge while preserving privacy. …”
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A social enterprise model for TB detection and treatment through the private sector in Pakistan by S. M. A. Zaidi, W. Z. Jamal, U. Ibrahim, S. Khowaja, A. J. Khan, J. Creswell

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Leveraging AI for early cholera detection and response: transforming public health surveillance in Nigeria by Adamu Muhammad Ibrahim, Mohamed Mustaf Ahmed, Shuaibu Saidu Musa, Usman Abubakar Haruna, Mohammed Raihanatu Hamid, Olalekan John Okesanya, Aishat Muhammad Saleh, Don Eliso Lucero-Prisno III

“…By integrating AI into Nigeria’s public health infrastructure, early detection and response can be improved, resource allocation optimized, and disease transmission minimized. …”
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Smart medical report: efficient detection of common and rare diseases on common blood tests by Ákos Németh, Ákos Németh, Gábor Tóth, Péter Fülöp, György Paragh, Bíborka Nádró, Zsolt Karányi, György Paragh, Zsolt Horváth, Zsolt Csernák, Zsolt Csernák, Erzsébet Pintér, Erzsébet Pintér, Dániel Sándor, Dániel Sándor, Gábor Bagyó, István Édes, János Kappelmayer, Mariann Harangi, Bálint Daróczy, Bálint Daróczy

“…We evaluated the diagnostic performance by (1) implementing ensemble learning (mean ROC-AUC.9293 and mean DOR 63.96); (2) assessing the model's sensitivity via risk scores to simulate its screening effectiveness; (3) analyzing the potential for early disease detection (30–270 days before clinical diagnosis) through creating historical patient timelines and (4) conducting validation on real-world clinical data in collaboration with Synlab Hungary, to assess the tool's performance in clinical setting.DiscussionUniquely, our model not only considers stable blood values but also tracks changes from baseline across 15 years of patient history. …”
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Advancing blood cell detection and classification: performance evaluation of modern deep learning models by Shilpa Choudhary, Sandeep Kumar, Pammi Sri Siddhaarth, Guntu Charitasri, Monali Gulhane, Nitin Rakesh, Feslin Anish Mon, Amal Al-Rasheed, Masresha Getahun, Ben Othman Soufiene

“…Because the present dataset will be an important resource for researchers and developers working on automatic blood cell detection and classification systems, we will make it publicly available under the open-access nature in order to accelerate the collaboration and progress in this field.…”
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Comparative evaluation of multimarker algorithms for early-stage HCC detection in multicenter prospective studies by Jinlin Hou, Thomas Berg, Arndt Vogel, Teerha Piratvisuth, Jörg Trojan, Enrico N. De Toni, Masatoshi Kudo, Katarina Malinowsky, Peter Findeisen, Johannes Kolja Hegel, Wenzel Schöning, Kairat Madin, Konstantin Kroeniger, Henry Lik-Yuen Chan, Ashish Sharma

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Framework for Addressing Imbalanced Data in Aviation with Federated Learning by Igor Kabashkin

“…The proposed framework combines specialized techniques for handling imbalanced data with privacy-preserving federated learning to enable effective collaboration while maintaining data security. The framework incorporates local resampling methods, cost-sensitive learning, and weighted aggregation mechanisms to improve minority class detection performance. …”
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SECURING INFORMATION CONFIDENTIALITY: A MATHEMATICAL APPROACH TO DETECTING CHEATING IN ASMUTH-BLOOM SECRET SHARING by Azhar Janjang Darmawan, Sugi Guritman, Jaharuddin Jaharuddin

“…This study aims to modify the Asmuth-Bloom scheme by introducing two detection mechanisms: Threshold Range Detection and Detection Parameter Verification, to identify and prevent collaborative fraudulent activities. …”
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Ternary-Emission Molecularly Imprinted Ratiometric Fluorescence Sensor and Kit for the Rapid and Visual Detection of Enrofloxacin by Siwu Liu, Jingyi Yan, Dani Sun, Siyuan Peng, Jinhua Li, Huaying Fan

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Dual Branch Encoding Feature Aggregation for Cloud and Cloud Shadow Detection of Remote Sensing Image by Naikang Shi, Haifeng Lin, Huiwen Ji, Min Xia

“…Specifically, two novel modules are introduced: the Dual-branch Lightweight Aggregation Module (DLAM), which fuses CNN and Transformer features in the early encoding stage and emphasizes key information through a feature weight allocation mechanism; and the Dual-branch Attention Aggregation Module (DAAM), which further integrates local and global features in the late encoding stage, improving the model’s differentiation performance between cloud and cloud shadow areas. The collaboration between DLAM and DAAM enables the model to efficiently learn multi-scale and spatially hierarchical information, thereby improving detection performance. …”
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Spatial Spillover Effect and Boundary Detection of County Tourism Economic Growth Promoting Economic Development by Liu Anle, Yang Chengyue, Ming Qingzhong, Wu Fenglin

“…Second, by studying this heterogeneity law from the perspective of spatial spillover distance, this study further explored the minimum, optimal, and maximum spatial spillover distance, aiming to facilitate regional spatial collaborative development in different areas, reduce the significant constraints of high time and transportation costs caused by geographical barriers, and provide theoretical and data references for the formulation of policies for high-quality regional collaborative development.…”
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Bisulfite‐free PCDHGB7 methylation in urine enables early noninvasive detection of urothelial carcinoma by Zhicong Yang, Qing Chen, Shihua Dong, Peng Xu, Zhanrui Mao, Yaping Dong, Wei Li, Wenxuan Li, Yang Han, Lihe Dai, Gehong Dong, Yong Zhang, Yinshan Li, Liang Cheng, Weimin Ci, Wenqiang Yu, Chuanliang Xu

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Allergenic potential of insect proteins: cross-reactivity, detection, degradation, and implications for food safety and labelling by Dongdong Ni

“…While significant progress has been made in allergen detection and risk assessment frameworks, there remains a lack of standardized guidelines for insect protein products. …”
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Real-Time Financial Fraud Detection Using Adaptive Graph Neural Networks and Federated Learning by Milad Rahmati

“…Additionally, data privacy regulations and institutional constraints limit collaborative fraud detection efforts, as financial organizations are often unable to share sensitive transactional data. …”
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Detecting Creative States From Emotional Cues: Insights From Speech With Focus on Text Modality by Sepideh Kalateh, Sanaz Nikghadam-Hojjati, Jose Barata

“…This paper explores the intersection of technology, human creativity and emotional expression by proposing a novel approach to detecting creative states through the analysis of emotional cues derived from speech. …”
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Privacy-preserving detection and classification of diabetic retinopathy using federated learning with FedDEO optimization by Dasari Bhulakshmi, Dharmendra Singh Rajput

“…Diabetic retinopathy (DR) is a major cause of blindness among adults worldwide. Detecting and classifying DR early is essential for timely treatment and prevention of vision loss. …”
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An Unsupervised Anomaly Detection Method for Railway Fasteners Based on Knowledge-Distilled Generative Adversarial Networks by Hongyan Chen, Zhiwei Li, Xinjie Xiao

“…To address the challenges of limited anomaly samples, irregular defect geometries, and complex operational conditions in rail fastener anomaly detection, this paper proposes an unsupervised anomaly detection method using a knowledge-distilled generative adversarial network. …”
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