Multisensor Data Fusion for Coastal Boundary Detection by Res-U-Net Implementation Using High-Resolution UAV Imagery by Qin Wang, Nyasha J. Kavhiza, Fakhrul Islam, Ilyas Ahmad Huqqani, Mohsin Abbas, Sanjoy Barman

“…Large-scale geospatial analytics and real-time coastal change detection can both benefit from the framework’s extension to multitemporal and multisensor data.…”
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Pixel-Based Change Detection in Moving-Camera Videos Using Twin Convolutional Features on a Data-Constrained Scenario by Luiz G. C. Tavares, Allan F. Da Silva, Rafael Padilla, Lucas A. Thomaz, Sergio L. Netto, Eduardo A. B. Da Silva

“…In addition, in order to deal with the challenges of training with high-dimensional data, a complexity-reducing training strategy that eliminates the need for large training sets while maintaining a robust detection performance was proposed. …”
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Outcomes for older people with screening-detected versus existing chronic kidney disease: a cohort study with data linkage by Anna K Forbes, José M Ordóñez-Mena, Winnie Mei, Clare J Taylor, Nicholas Jones, Jennifer A Hirst, FD Richard Hobbs

“…Aim: To compare the risk of adverse health outcomes between people with an existing diagnosis of CKD and those identified through screening, and identify factors associated with mortality in CKD. Design & setting: Prospective cohort study of 892 primary care patients aged ≥60 years with CKD (existing and screening detected) in Oxfordshire, with data linkage to civil death registry and secondary care. …”
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N-NOSE Proves Effective for Early Cancer Detection: Real-World Data from Third-Party Medical Institutions by Nobushige Nakajo, Hideyuki Hatakeyama, Masayo Morishita, Eric di Luccio

“…This opinion manuscript evaluates nematode cancer testing, a novel urine-based screening test using the nematode <i>Caenorhabditis elegans</i>, for its potential to improve early cancer detection rates, based on real-world data published in a Japanese journal. …”
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Toward automated plantar pressure analysis: machine learning-based segmentation and key point detection across multicenter data by Carlo Dindorf, Jonas Dully, Steven Simon, Dennis Perchthaler, Stephan Becker, Hannah Ehmann, Christian Diers, Christoph Garth, Michael Fröhlich

“…Leveraging these strengths, this work introduces a novel framework that integrates multicenter plantar pressure data for both segmentation and landmark detection, offering practical value in clinical and research settings by enabling standardized, automated analyses across varying hardware configurations.…”
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Synergizing advanced algorithm of explainable artificial intelligence with hybrid model for enhanced brain tumor detection in healthcare by Kamini Lamba, Shalli Rani, Mohammad Shabaz

“…As understanding reasoning behind their predictions is still a great challenge for the healthcare professionals and raised a great concern about their trustworthiness, interpretability and transparency in clinical settings. Thus, an advanced algorithm of explainable artificial intelligence (XAI) has been synergized with hybrid model comprising of DenseNet201 network for extracting the most important features based on the input Magnetic resonance imaging (MRI) data following supervised algorithm, support vector machine (SVM) to distinguish distinct types of brain scans. …”
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Lung Cancer Prediction Using an Enhanced Neutrosophic Set Combined with a Machine Learning Approach by Vakeel A. Khan, Asheesh Kumar Yadav, Mohammad Arshad, Nadeem Akhtar

“…Lung cancer (LC) remains one of the most lethal diseases globally, necessitating the development of advanced predictive models for early detection and accurate diagnosis. Traditional classification techniques often struggle with uncertainty and indeterminacy in medical data, which can lead to misdiagnosis and reduced diagnostic reliability. …”
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Outlier detection algorithm based on fast density peak clustering outlier factor by Zhongping ZHANG, Sen LI, Weixiong LIU, Shuxia LIU

“…For the problem that peak density clustering algorithm requires human set parameters and high time complexity, an outlier detection algorithm based on fast density peak clustering outlier factor was proposed.Firstly, k nearest neighbors algorithm was used to replace the density peak of density estimate, which adopted the KD-Tree index data structure calculation of k close neighbors of data objects, and then the way of the product of density and distance was adopted to automatic selection of clustering centers.In addition, the centripetal relative distance and fast density peak clustering outliers were defined to describe the degree of outliers of data objects.Experiments on artificial data sets and real data sets were carried out to verify the algorithm, and compared with some classical and novel algorithms.The validity and time efficiency of the proposed algorithm are verified.…”
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Outlier detection algorithm based on fast density peak clustering outlier factor by Zhongping ZHANG, Sen LI, Weixiong LIU, Shuxia LIU

“…For the problem that peak density clustering algorithm requires human set parameters and high time complexity, an outlier detection algorithm based on fast density peak clustering outlier factor was proposed.Firstly, k nearest neighbors algorithm was used to replace the density peak of density estimate, which adopted the KD-Tree index data structure calculation of k close neighbors of data objects, and then the way of the product of density and distance was adopted to automatic selection of clustering centers.In addition, the centripetal relative distance and fast density peak clustering outliers were defined to describe the degree of outliers of data objects.Experiments on artificial data sets and real data sets were carried out to verify the algorithm, and compared with some classical and novel algorithms.The validity and time efficiency of the proposed algorithm are verified.…”
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Cauliflower leaf diseases: A computer vision dataset for smart agricultureMendeley Data by Sabbir Hossain Durjoy, Md. Emon Shikder, Md Mehedi Hasan Shoib, Md Hasan Imam Bijoy

“…This data set also has immense potential for real-time and practical use. …”
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Geospatial Data and Google Street View Images for Monitoring Kudzu Vines in Small and Dispersed Areas by Alba Closa-Tarres, Fernando Rojano, Michael P. Strager

“…The INHABIT database from the United States Geological Survey (USGS) provided geospatial data of Kudzu vines and Google Street View (GSV) a set of images. …”
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Comparative Analysis of Different Display Technologies for Defect Detection in 3D Objects by Vasil Kozov, Ekaterin Minev, Magdalena Andreeva, Tzvetomir Vassilev, Rumen Rusev

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Implications to basin evolution from the interpretation of superficial and buried geological features from remote sensing and magnetic data sets, Lower and Middle Benue Trough, Nig... by E.Y. Yenne, C. Green, T. Torvela

“…We used high-resolution aeromagnetic data and, Landsat 8 and ASTER data sets to interpret surface and near-surface lithologies, particularly igneous bodies, within the Benue Trough, Nigeria, with the aim of better constraining the basin evolution in time and space. …”
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Research on defect perception model of distribution network based on big data analysis and waveform matching algorithm by LIN Kaifeng, LI Yiming, ZHANG Bo, YANG Changyu, ZHU Zeting, YANG Zhenda

“…Through utilizing the data acquisition capability of the distribution terminal unit (DTU) of the distribution line, the protection settings are reasonably set to collect fault flashover information without affecting FA functions, and analyze the signal waveform characteristics to extract fault waveform characteristics for equipment defect identification; The distribution network defect perception model is established based on the waveform matching algorithm, the identification of fault waveform is trained and learned, and the analytic hierarchy process (AHP) algorithm is used to quantify the risk assessment. …”
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Metric-Based Few-Shot Transfer Learning Approach for Voice Pathology Detection by Jong-Ho Won, Deok-Hwan Kim

“…The main contributions of this study include the implementation of few-shot learning (FSL) in voice pathology detection, addressing data scarcities and class imbalances through its integration with transfer learning. …”
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Self-Supervised Drift-Resilient Classification for Time Series Industrial Anomaly Detection by Myung-Kyo Seo, Byeong Hoon Yoon, Junseung Ryu, Hyung Ju Hwang

“…Unlike conventional fault classification approaches that identify predefined defect types, our model focuses on detecting subtle, evolving anomalies in time-series vibration data. …”
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YOLO-MMCE algorithm for visual detection of engineering vehicles in high-consequence area by Huimei SUN, Lu LIU, Degang WANG, Taiyong WANG

“…Methods The Mosaic + Mixup combined data augmentation approach was adopted to improve the model's adaptability to diverse scenarios and strengthen its robustness and generalization in intricate real-world settings and ambiguous conditions. …”
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Detection Method for Three-Phase Electricity Theft Based on Multi-Dimensional Feature Extraction by Wei Bai, Lan Xiong, Yubei Liao, Zhengyang Tan, Jingang Wang, Zhanlong Zhang

“…The advent of smart grids has facilitated data-driven methods for detecting electricity theft, with a preponderance of research efforts focused on user electricity consumption data. …”
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Multi-Temporal Remote Sensing Satellite Data Analysis for the 2023 Devastating Flood in Derna, Northern Libya by Roman Shults, Ashraf Farahat, Muhammad Usman, Md Masudur Rahman

“…The high-resolution GEOEYE-1 images were used for precise change detection using image differencing (pixel-to-pixel comparison and geographic object-based image analysis (GEOBIA) for extracting building), whereas Sentinel-2 data were employed for the classification and further change detection by classified images. …”
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Detecting outliers in segmented genomes of flu virus using an alignment-free approach by Mosaab Daoud

“…In this paper, we propose a new approach to detecting outliers in a set of segmented genomes of the flu virus, a data set with a heterogeneous set of sequences. …”
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