Decision-Aid Framework for Face Authentication Detection Using ResNext50 and BiLSTM to Enhance Media Integrity by Ayat Abd-Muti Alrawahneh, Siti Norul Huda Sheikh Abdullah, Tarik Abuain, Sharifah Nurul Asyikin Syed Abdullah, Sarah Khadijah Taylor, Nur Hanis Sabrina Suhaimi

“…Unlike conventional approaches focusing solely on spatial features, the proposed hybrid model incorporates temporal analysis, enabling the detection of subtle manipulations distributed across sequential video frames. …”
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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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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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A Modified MobileNetv3 Coupled With Inverted Residual and Channel Attention Mechanisms for Detection of Tomato Leaf Diseases by Rubina Rashid, Waqar Aslam, Romana Aziz, Ghadah Aldehim

“…This feature integration and data analysis scheme are then deployed to provide a tomato leaf disease detection Android app. …”
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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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Automatic Detection of Occluded Main Coronary Arteries of NSTEMI Patients with MI-MS ConvMixer + WSSE Without CAG by Mehmet Cagri Goktekin, Evrim Gul, Tolga Çakmak, Fatih Demir, Mehmet Ali Kobat, Yaman Akbulut, Ömer Işık, Zehra Kadiroğlu, Kürşat Demir, Abdulkadir Şengür

“…The features extracted from this model were classified with the WSSE algorithm, and an accuracy of 88.72% was achieved. …”
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MORIX: Machine learning-aided framework for lethality detection and MORtality inference with eXplainable artificial intelligence in MAFLD subjects by Domenico Lofù, Paolo Sorino, Tommaso Colafiglio, Caterina Bonfiglio, Rossella Donghia, Gianluigi Giannelli, Angela Lombardi, Tommaso Di Noia, Eugenio Di Sciascio, Fedelucio Narducci

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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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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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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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A Study on the Extraction of Satellite Image Information for Two Types of Coastal Fishery Facility Fish Cages and Rafts Influenced by Clouds and Vessels by Ao Chen, Jialu Yu, Junbo Zhang, Gangyi Yu, Rong Wan

“…This study proposes a multi-feature and rule-based object-oriented image classification (MROIC) model, integrating spatial-spectral enhancement techniques with object-based image analysis classification methods. …”
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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 Dual-Branch Deep Learning Framework Combining Xception and ResNet for Accurate Lung and Colon Cancer Detection by Chandrasekar Venkatachalam, Priyanka Shah

“…Lung and colon cancers are among the leading causes of cancer-related deaths worldwide. Early detection significantly enhances survival rates, but traditional diagnostic methods, which rely on manual analysis of histopathological images, are labor-intensive, error-prone, and inconsistent. …”
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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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Effective multimodal hate speech detection on Facebook hate memes dataset using incremental PCA, SMOTE, and adversarial learning by Emmanuel Ludivin Tchuindjang Tchokote, Elie Fute Tagne

“…Link to the code and dataset below:https://github.com/ludivintchokote/HatePostDetection…”
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Wavelet-Analysis of Cardiosignals Using Matlab by B. F. Badalyan, H. A. Gomtsyan, S. G. Gomtsyan

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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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Quantum-classical deep learning hybrid architecture with graphene-printed low-cost capacitive sensor for essential tremor detection by Javier Villalba-Díez, Ana González-Marcos

“…Abstract This study presents a novel hardware and software architecture combining capacitive sensors, quantum-inspired algorithms, and deep learning applied to the detection of Essential Tremor. At the core of this architecture are graphene-printed capacitive sensors, which provide a cost-effective and efficient solution for tremor data acquisition. …”
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