Comparison of different classification of metabolic syndrome in the Kyrgyz ethnic group by Ch. B. Moldokeeva, O. S. Lunegova, A. S. Kerimkulova, A. E. Mirrakhimov, S. S. Abilova, A. A. Bairamukova, Yu. V. Zalesskaya, E. M. Mirrakhimov

“…Goal: Comparison of different classification of metabolic syndrome (MS) in Kyrgyz ethnic group. …”
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Group feature calibration for sound event detection by Yanzhen Ren, Wuyang Liu, Chenyu Liu, Tingting Zhu

“…Abstract Sound Event Detection (SED) is a pivotal task in audio signal processing with widespread applications, requiring the classification and temporal localization of sound events. …”
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Effective and Reliable Malware Group Classification for a Massive Malware Environment by Taejin Lee, Jin Kwak

“…This paper proposes a scheme for the detection and group classification of malware, some measures to improve the dependability of classification using the local clustering coefficient, and the technique for selecting and managing the leading malware for each group to classify them cost-effectively in a massive malware environment. …”
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Phytoplankton group classification by integrating trait information and observed environmental thresholds by Hoang Vuong Dang, Kermode Stephanie, Peisheng Huang, Cayelan C. Carey, Matthew R. Hipsey

“…Third, we applied K-prototype clustering for group classification based on the identified thresholds and associated traits. …”
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Explainable handcrafted features for mitotic event detection and classification by Panason Manorost, Thomas Deckers, Veerle Bloemen, Jean Marie Aerts

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Video inter-frame tampering detection algorithm fusing multiple features by Hui XIAO, Bin WENG, Tianqiang HUANG, Han PU, Zehui HUANG

Subjects: “…video tamper detection…”
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Transformer-based ECG classification for early detection of cardiac arrhythmias by Sunnia Ikram, Amna Ikram, Harvinder Singh, Malik Daler Ali Awan, Sajid Naveed, Isabel De la Torre Díez, Henry Fabian Gongora, Thania Candelaria Chio Montero

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Group Abnormal Behaviour Detection Algorithm Based on Global Optical Flow by Yu Hao, Ying Liu, Jiulun Fan, Zhijie Xu

“…The network uses two network branches to learn spatial dimension information and temporal dimension information, respectively, and uses short- and long-time neural network to model the dependency relationship between long-time video frames, so as to obtain the final behaviour classification results. Simulation test results show that the proposed method can achieve good recognition effect on multiple datasets, and the performance of abnormal behaviour detection can be significantly improved by using interframe motion information.…”
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Cells Grouping Detection and Confusing Labels Correction on Cervical Pathology Images by Wenbo Pang, Yi Ma, Huiyan Jiang, Qiming Yu

“…Specifically, we utilize clinical prior knowledge to break down the detection task into multiple sub-tasks for cell grouping detection, aiming to more effectively learn the specific structure of cells. …”
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A complete benchmark for polyp detection, segmentation and classification in colonoscopy images by Yael Tudela, Mireia Majó, Neil de la Fuente, Adrian Galdran, Adrian Krenzer, Frank Puppe, Amine Yamlahi, Thuy Nuong Tran, Bogdan J. Matuszewski, Kerr Fitzgerald, Cheng Bian, Junwen Pan, Shijle Liu, Gloria Fernández-Esparrach, Aymeric Histace, Jorge Bernal

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Gas Detection and Classification Using Neural Network Based Gas Sensors by Munaf Ismail, Sri Arttini Dwi Prasetyowati

“…For this experiment, ANN is used as a liquid classification in grouping alcoholic and non-alcoholic liquids. …”
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Artificial Intelligence Classification for Detecting and Grading Lumbar Intervertebral Disc Degeneration by Wongthawat Liawrungrueang, Watcharaporn Cholamjiak, Peem Sarasombath, Khanathip Jitpakdee, Vit Kotheeranurak

“…Conclusions: The AI-based classification model exhibits high accuracy, sensitivity, and specificity in detecting and grading lumbar IDD using the Pfirrmann grading. …”
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GeNIS: A modular dataset for network intrusion detection and classificationZenodo by Miguel Silva, Daniela Pinto, João Vitorino, José Gonçalves, Eva Maia, Isabel Praça

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Multimodal image fusion for ich detection and classification using parallel Dl models by Sri Sangepu Nagaraju, S. Prince Mary, V. Pavani Chandra, Nandam Gayatri

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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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A Novel CNN-Based Framework for Detection and Classification of Power Quality Disturbances: Exploring Multi-Class Versus Multi-Label Classification by Aleksandra Zlatkova, Dimitar Taskovski

“…The detection and classification of power quality (PQ) disturbances remains a significant challenge because of the rapid integration of renewable energy sources (RES), widespread use of power electronics, and increasing prevalence of sensitive microcontrollers. …”
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Linear pattern detection of building groups by integrating dynamic snake convolution with YOLO11 by Xiao Wang, Yue Wu, Longfei Cui, Haizhong Qian, Bohao Li, Xu Wang

“…Accurately detecting the patterns of building groups is the premise and foundation of building generalization. …”
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Automatic detection and classification process for concrete defects in deteriorating buildings based on image data by Suhyun Kang, Seungho Kim, Sangyong Kim

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Detection and classification of breast cancer in mammographic images with fine-tuned convolutional neural networks by Huong Hoang Luong, Hai Thanh Nguyen, Nguyen Thai-Nghe

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Interfered feature elimination coupled with feature group selection for wound infection detection by electronic nose. by Jia Liu, Jinglei Zhang, Shaoqi Zhang, Kaiwei Li, Xiang Li, Shuo Zhang, Hang Gu, Zhen Chen, Chao Liu, Nan Zhang, Tong Sun

“…For this issue, we proposed a new sensor array optimization algorithm named Interfered Feature Elimination coupled with Feature Group Selection (IFE-FGS). In this method, the IFE algorithm first removed the bad sensor features; then the FGS algorithm determined the optimized sensor combination by gradually selecting the features in groups. …”
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