Effective Gait Abnormality Detection in Parkinson’s Patients for Multi-Sensors Surveillance System by Abdulwahab Alazeb, Mouazma Batool, Naif Al Mudawi, Mohammed S. Alshehri, Sultan Almakdi, Nouf Abdullah Almujally, Asaad Algarni

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Void Detection of Airport Concrete Pavement Slabs Based on Vibration Response Under Moving Load by Xiang Wang, Ziliang Ma, Xing Hu, Xinyuan Cao, Qiao Dong

“…The results revealed that the RF model achieved strong predictive performance, with a high correlation between key features and void characteristics. This work demonstrates the feasibility of integrating simulation analysis, signal feature extraction, and machine learning to support intelligent diagnostics of concrete pavement health.…”
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Classification and Detection of Rumors Related to COVID-19 Using Machine Learning-Based Smart Techniques by Yancheng Yang, Junqiao Zhai, Shah Nazir

“…After studying different information detection techniques, various features have been identified from the literature. …”
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Objective Detection of Newborn Infant Acute Procedural Pain Using EEG and Machine Learning Algorithms by Jean‐Michel Roué, Amir Avnit, Behnood Gholami, Wassim M. Haddad, Kanwaljeet J. S. Anand

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Development of a deep learning model for automated detection of calcium pyrophosphate deposition in hand radiographs by Thomas Hügle, Elisabeth Rosoux, Guillaume Fahrni, Deborah Markham, Tobias Manigold, Fabio Becce

“…The MCP-2-alone and MCP-3-alone models exhibited mean AUROCs of 0.78–0.87, but lower mean AUPRs of 0.29–0.47. Heatmap analysis revealed activation in the regions of interest for positive cases (true and false positives), but unexpected highlights were encountered possibly due to correlated features in different hand regions.ConclusionA combined deep-learning model detecting CPPD at the TFCC and MCP-2/3 joints in hand radiographs provides the highest diagnostic performance. …”
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Efficient automated detection of power quality disturbances using nonsubsampled contourlet transform & PCA-SVM by Pampa Sinha, Kaushik Paul, Asit Mohanty, IM Elzein, Chandra Sekhar Mishra, Mohamed Metwally Mahmoud, Daniel Eutyche Mbadjoun Wapet, Abdulrahman Al Ayidh, Ahmed Althobaiti, Hany S Hussein, Thamer AH Alghamdi, Ahmed M Ewais

“…Principal component analysis (PCA) is applied to the extracted features to reduce dimensionality and improve feature separability. …”
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Nonlinear Model for Condition Monitoring and Fault Detection Based on Nonlocal Kernel Orthogonal Preserving Embedding by Bo She, Fuqing Tian, Weige Liang, Gang Zhang

“…Compared with KONPE and KPCA, NLKOPE combines both the advantages of KONPE and KPCA, and NLKOPE is also more powerful in extracting potential useful features in nonlinear data set than NLOPE. For the purpose of condition monitoring and fault detection, monitoring statistics are constructed in feature space. …”
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Detection of Ship Wakes in Dynamic Sea Surface Video Sequences: A Data-Driven Approach by Chengcheng Yu, Yanmei Zhang, Meifang Xiao, Zhibo Zhang

“…In order to enhance the detection of maritime vessel targets, considering the causal relationship between the motion of vessels and their wakes, as well as the characteristics of ship wakes such as large diffusion range and distinctive features, this paper proposes a data-driven method based on Dynamic Mode Decomposition (DMD) for detecting and analyzing ship wakes in sea surface videos. …”
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Detection of Bipolar Disorder and Schizophrenia Employing Bayesian-Optimized Grad-CAM-Driven Deep Learning by Osman Tayfun Bişkin, Cemre Candemir, Mustafa Alper Selver

“…Deep learning (DL) has emerged as a transformative tool in neuroimaging analysis, offering the ability to automatically extract intricate features from large datasets. …”
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Enhancing smart contract security: Leveraging pre‐trained language models for advanced vulnerability detection by Fei He, Fei Li, Peili Liang

“…Traditional methods for detecting these vulnerabilities, including manual inspections and automated static analysis, are plagued by issues such as high rates of false positives and overlooked security flaws. …”
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Feature Extraction and Classification of Power Quality Disturbances Using Optimized Tunable-Q Wavelet Transform and Incremental Support Vector Machine by Indu Sekhar Samanta, Pravat Kumar Rout, Kunjabihari Swain, Satyasis Mishra, Murthy Cherukuri

“…Lastly, to test and evaluate the performance of the proposed approach, twelve types of PQDs including noise and multiple occurrences are considered. The comparative analysis with other popular methods reflects the better performance of the proposed approach and justifies its use for PQ detection and classification purposes in real-time​ conditions.…”
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Automated detection of Parkinson’s disease using improved linknet-ghostnet model based on handwriting images by P Pradeep, J Kamalakannan

“…Then, modified PHOG, Deep features and Shape features are extracted. Finally, detection is performed using hybrid Improved LinkNet and Ghostnet models, termed (ILN-GNet), whose outcomes indicate if the individual is healthy or affected. …”
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Domain Generalized Open-Set Fault Detection and Diagnosis for Belt Conveyor Systems With Prototype Learning by Jinyou Kim, Ilkyu Yi, Young-Joo Suh

“…Various approaches have been explored for fault detection and diagnosis (FDD) in belt conveyor systems, but research specifically addressing the unique features of these systems and providing a comprehensive analysis remains limited. …”
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A Systematic Review of Surface Electromyography in Sarcopenia: Muscles Involved, Signal Processing Techniques, Significant Features, and Artificial Intelligence Approaches by Alessandro Leone, Anna Maria Carluccio, Andrea Caroppo, Andrea Manni, Gabriele Rescio

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A comprehensive study of non-destructive localization of structural features in metal plates using single and multimodal Lamb wave excitations by Silitonga Dicky J., Declercq Nico F., Walaszek Henri, Vu Quang A., Saidoun Abdelkrim, Samet Naim, Ndiaye Elhadji Barra, Thabourey Jérôme

“…They provide critical insights into the method’s ability to deliver precise and efficient detection of structural anomalies despite inherent challenges in signal interpretation and analysis.…”
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STIED: a deep learning model for the spatiotemporal detection of focal interictal epileptiform discharges with MEG by Raquel Fernández-Martín, Alfonso Gijón, Odile Feys, Elodie Juvené, Alec Aeby, Charline Urbain, Xavier De Tiège, Vincent Wens

“…Abstract Magnetoencephalography (MEG) allows the non-invasive detection of interictal epileptiform discharges (IEDs). …”
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A Hybrid Model of Feature Extraction and Dimensionality Reduction Using ViT, PCA, and Random Forest for Multi-Classification of Brain Cancer by Hisham Allahem, Sameh Abd El-Ghany, A. A. Abd El-Aziz, Bader Aldughayfiq, Menwa Alshammeri, Malak Alamri

“…ViT was employed for feature extraction, PCA for feature dimension reduction, and RF for brain tumor classification. …”
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Automated Fovea Detection in Spectral Domain Optical Coherence Tomography Scans of Exudative Macular Disease by Jing Wu, Sebastian M. Waldstein, Alessio Montuoro, Bianca S. Gerendas, Georg Langs, Ursula Schmidt-Erfurth

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Camscribe: Enhanced Dashcam Video Descriptions Through Multimodal Spatiotemporal and Object Detection for Autonomous Vehicles by Muhammad Rafiq, Mankyu Sung, Ghazala Rafiq, Gyu Sang Choi

“…The model training was conducted on the DeepRide dashcam dataset, leveraging multimodal video features for robust performance. The efficacy of our proposed methodology is validated through extensive qualitative, quantitative evaluation and ablation analysis. …”
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Conformal On-Body Antenna System Integrated with Deep Learning for Non-Invasive Breast Cancer Detection by Marwa H. Sharaf, Manuel Arrebola, Khalid F. A. Hussein, Asmaa E. Farahat, Álvaro F. Vaquero

“…Breast cancer detection through non-invasive and accurate techniques remains a critical challenge in medical diagnostics. …”
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