Modeling of potential field data for detecting structural and tectonic framework of Esh El Mellaha area, Red Sea, Egypt by Abdelbaset M. Abudeif, Zakaria Hamimi, Gaber M. Gaber, Adel Kotb, Nasir Alarifi, Stefano Bellucci, Marwa M. Masoud

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Development and validation of a multimodal automatic interictal epileptiform discharge detection model: a prospective multi-center study by Nan Lin, Lian Li, Weifang Gao, Peng Hu, Gonglin Yuan, Heyang Sun, Fang Qi, Lin Wang, Shengsong Wang, Zi Liang, Haibo He, Yisu Dong, Zaifen Gao, Xiaoqiu Shao, Liying Cui, Qiang Lu

“…Conclusions The multimodal IED detection model, which integrates video and EEG features, demonstrated high precision and robustness. …”
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Application of Machine Learning Models for the Early Detection of Metritis in Dairy Cows Based on Physiological, Behavioural and Milk Quality Indicators by Karina Džermeikaitė, Justina Krištolaitytė, Ramūnas Antanaitis

“…Five classification models—partial least squares discriminant analysis (PLS-DA), random forest (RF), support vector machine (SVM), neural network (NN), and an Ensemble model—were developed using standardised features and stratified 80/20 training/test splits. …”
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A Sensitive and Selective Sensor Based on Orthorhombic Copper Molybdate Decorated on Reduced Graphene Oxide for the Detection of Promethazine Hydrochloride by Venkatachalam Vinothkumar, Yellatur Chandra Sekhar, Shen-Ming Chen, Natesan Manjula, Tae Hyun Kim

“…Hence, establishing a sensitive, accurate, and efficient detection approach to detect PMH in human samples is imperative. …”
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Automated detection of diabetic retinopathy lesions in ultra-widefield fundus images using an attention-augmented YOLOv8 framework by Lei-Si Hu, Jie Wang, Heng-Ming Zhang, Hai-Yu Huang

“…The performances of the three models—YOLOv8, YOLOv8+ convEMA, and YOLOv8+ convSimAM—were systematically compared.ResultsA comparative analysis of the three models revealed that the original YOLOv8 model suffers from missed detection issues, achieving a precision of 0.815 for hemorrhage spot detection. …”
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A Low-Cost AI-Powered System for Early Detection of Diabetic Retinopathy and Ocular Diseases in Resource Limited Settings by Harpreet Vohra, Mohammad Kamrul Hasan, Siti Norul Huda Sheikh Abdullah, Shayla Islam, Abdul Hadi Abd Rahman, Shreya N. Bhojake, Dhruv Bobal, Awal Kaur Gandhi, Muskan, Chandramohan Dhasarathan

“…This paper introduces an intelligent diabetic retinopathy detection system to enhance affordability, accessibility, and early diagnosis. …”
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Robust molecular subgrouping and reference-free aneuploidy detection in medulloblastoma using low-depth whole genome bisulfite sequencing by Dean Thompson, Jemma Castle, Martin Sill, Stefan M. Pfister, Simon Bailey, Debbie Hicks, Steven C. Clifford, Edward C. Schwalbe

“…Abstract Medulloblastoma comprises four principal molecular disease groups and their component subgroups, each with distinct molecular and clinical features. Group assignment is currently achieved diagnostically using Illumina DNA methylation microarray. …”
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Automated Detection of High Frequency Oscillations in Intracranial EEG Using the Combination of Short-Time Energy and Convolutional Neural Networks by Dakun Lai, Xinyue Zhang, Kefei Ma, Zichu Chen, Wenjing Chen, Heng Zhang, Han Yuan, Lei Ding

“…In addition, the time occurrence of each transient event of the HFOs can be identified to be potentially useful for further seizure analysis. In conclusion, this automated detection of the HFOs combing the STE and the CNN could allow analyzing large amounts of data in a short time while assuring a relatively higher accuracy and, thus, would potentially serve to provide a clinically useful tool.…”
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Efficient and Accurate Zero-Day Electricity Theft Detection from Smart Meter Sensor Data Using Prototype and Ensemble Learning by Alyaman H. Massarani, Mahmoud M. Badr, Mohamed Baza, Hani Alshahrani, Ali Alshehri

“…Smart meter data is compressed using Principal Component Analysis (PCA) and K-means clustering to extract representative consumption patterns, i.e., prototypes, achieving a 92% reduction in dataset size while preserving critical anomaly-relevant features. …”
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TCCFNet: a semantic segmentation method for mangrove remote sensing images based on two-channel cross-fusion networks by Lixiang Fu, Yaoru Wang, Shulei Wu, Jiasen Zhuang, Zhongqiang Wu, Jian Wu, Huandong Chen, Yukai Chen

“…ResNet improves the identification of small targets, while Swin Transformer enhances the segmentation of large-scale features. Additionally, a Cross Integration Module (CIM) is incorporated to strengthen multi-scale feature fusion and enhance adaptability to complex scenarios. …”
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The aspects of classification and examination of cigarettes in the course of forensic commodity examination by N. V. Kryvoruchko, T. S. Kyrychenko

“…The examination takes into account key product characteristics, such as physical and chemical composition, consumer properties, packaging and labelling features, as well as compliance with the declared characteristics. …”
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Using Electrooculography and Electrodermal Activity During a Cold Pressor Test to Identify Physiological Biomarkers of State Anxiety: Feature-Based Algorithm Development and Valida... by Jadelynn Dao, Ruixiao Liu, Sarah Solomon, Samuel Aaron Solomon

“…Shapley additive explanations (SHAP) were used to interpret and refine our model, enabling a robust understanding of the biomarkers that correlate strongly with s-anxiety. ResultsBLINKEO feature analysis achieved a classification accuracy of 98.17% and F1 ConclusionsThese results suggest that a combined analysis of EOG and EDA data offers significant improvements in detecting real-time anxiety markers, underscoring the potential of wearables in personalized health monitoring and mental health intervention strategies. …”
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Three-dimensional graph reconstruction of filamentous structures from z-stack images by Oscar Sten, Emanuela Del Dottore, Marilena Ronzan, Nicola Pugno, Barbara Mazzolai

“…It allows simpler sample preparation, reduces acquisition system complexity, and performs non-invasive analysis compared to other techniques. In this study, we propose an algorithm that, given a Z-stack image of a filamentous structure, 1) detects the filaments in each focal plane producing a binary skeleton, 2) smoothes the resulting graph into a less dense homeomorphic graph, 3) identifies the corresponding nodes from the different focal planes by solving an optimal matching problem, 4) estimates the relative depth of the filament at each node coordinate through a shape-from-focus approach, and 5) identifies shallow overlaps through a criterion based on steerable filters. …”
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Brain Tumour Segmentation and Grading Using Local and Global Context-Aggregated Attention Network Architecture by Ahmed Abdulhakim Al-Absi, Rui Fu, Nadhem Ebrahim, Mohammed Abdulhakim Al-Absi, Dae-Ki Kang

“…Brain tumours (BTs) are among the most dangerous and life-threatening cancers in humans of all ages, and the early detection of BTs can make a huge difference to their treatment. …”
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Research on bolt loosening recognition based on sound signal and GA-SVM–RFE by Li-Ye Hou, De-Bing Zhuo

“…Abstract In response to the difficulties faced in detecting bolt connection damage in steel truss structures, this paper proposes a bolt loosening identification method based on sound signal analysis, a Genetic Algorithm-Optimized Support Vector Machine (GA-SVM), and Recursive Feature Elimination (RFE). …”
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A lightweight multi-path convolutional neural network architecture using optimal features selection for multiclass classification of brain tumor using magnetic resonance images by Amreen Batool, Yung-Cheol Byun

“…Early disease identification, improved survival rates, and less reliance on professional MRI analysis is possible with computer-aided diagnostic (CAD) systems using advanced technology such as Convolutional Neural Networks (CNN), which have successfully detected brain tumors in MRI images. …”
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TU-DAT: A Computer Vision Dataset on Road Traffic Anomalies by Pavana Pradeep Kumar, Krishna Kant

“…It includes spatiotemporal annotations and structured metadata such as vehicle trajectories, collision types, and road conditions. These features enable robust model training for anomaly detection, spatial reasoning, and vision–language model (VLM) enhancement. …”
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A Deep Learning Approach for Identifying Intentional AIS Signal Tampering in Maritime Trajectories by Xiangdong Lv, Ruhao Jiang, Chao Chang, Nina Shu, Tao Wu

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Damage identification based on the inner product matrix and parallel convolution neural network for frame structure by Yingying He, Ji Feng, Baogang Sun, Feixue Wang, Likai Zhang, Jidi Jiang

“…To overcome this limitation, this study proposes a novel approach that combines an inner product matrix (IPM) with a parallel CNN (IPM-PCNN) to extract multidimensional features for detecting structural damage in a steel frame structure. …”
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Aerolysin Nanopores for Single-Molecule Analysis by Yun Zhang, Chan Cao

“…Biological nanopores have become powerful tools for single-molecule analysis in many fields, including metal ion detection, single-molecule chemistry, polymer size discrimination, nucleic acid sequencing, and protein/peptide/glycan analysis. …”
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