Face Spoof Attack Recognition Using Discriminative Image Patches by Zahid Akhtar, Gian Luca Foresti

“…Though several face antispoofing or liveness detection methods (which determine at the time of capture whether a face is live or spoof) have been proposed, the issue is still unsolved due to difficulty in finding discriminative and computationally inexpensive features and methods for spoof attacks. …”
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Human Action Recognition Based on The Skeletal Pairwise Dissimilarity by E.E. Surkov, O.S. Seredin, A.V. Kopylov

“…A convolutional neural network based on the ResNetV2 with the SE-block is proposed to solve the activity recognition problem. SE-block allows to detect inter-channel dependencies and selecting the most important features. …”
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Adaptive machine learning framework: Predicting UHPC performance from data to modelling by Yinzhang He, Shaojie Gao, Yan Li, Yongsheng Guan, Jiupeng Zhang, Dongliang Hu

“…The framework has several key modules: data preprocessing, feature selection, outlier detection, model training, hyperparameter optimization, and model interpretation. …”
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Quantitation of Phosphotyrosine Signals in Human Prostate Cell Adhesion Sites by Anne E. Cress

“…The signals within the cell adhesion sites are quantitated using the density slice and particle analysis features of the software. The immunohistochemistry results are compared with detection of phosphotyrosine signals using a standard Western blotting procedure with whole cell lysates. …”
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Application of MobileNetV2 and SVM Combination for Enhanced Accuracy in Pneumonia Classification by Eka Putra Agus Meindiawan, Muljono Muljono

“…Modern technologies, such as machine learning, offer significant potential in improving the automatic detection of pneumonia through chest X-ray (CXR) image analysis. …”
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Information Merging for Improving Automatic Classification of Electrical Impedance Mammography Images by Jazmin Alvarado-Godinez, Hayde Peregrina-Barreto, Delia Irazú Hernández-Farías, Blanca Murillo-Ortiz

“…This study evaluated the impact of these integration techniques on EIM image analysis, aiming to improve the accuracy and reliability of computational diagnostic models for breast cancer detection. …”
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Epilepsy EEG Seizure Prediction Based on the Combination of Graph Convolutional Neural Network Combined with Long- and Short-Term Memory Cell Network by Zhejun Kuang, Simin Liu, Jian Zhao, Liu Wang, Yunkai Li

“…While enriching the input of LSTM, it also makes full use of the information hidden in the EEG signals. In the automatic detection of epileptic seizures based on neural networks, due to the strong non-stationarity and large background noise of the EEG signal, the analysis and processing of the EEG signal has always been a challenging research. …”
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Human fall direction recognition in the indoor and outdoor environment using multi self-attention RBnet deep architectures and tree seed optimization by Awais Khan, Jung-Yeon Kim, Chomyong Kim, Muhammad Attique Khan, Hyojin Shin, Jiyoung Woo, Yunyoung Nam

“…The 7-RBNet and 9-RBNet self-attention models demonstrated superior accuracy and precision rates, leading us to exclude the 3-RBNet self model from further analysis. To optimize feature selection and improve classification performance while reducing computational costs, we employed the tree seed algorithm on the self-attention features of 7-RBNet and 9-RBNet self-attention models. …”
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Heart abnormality classification using ECG and PCG recordings with novel PJM-DJRNN by Nadikatla Chandrasekhar, Sujatha Canavoy Narahari, Sreedhar Kollem, Samineni Peddakrishna, Archana Penchala, Babji Prasad Chapa

“…HD causes more number of deaths per year. Hence, the early detection of HD is needed to increase the survival rate. …”
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Investigation on potential bias factors in histopathology datasets by Farnaz Kheiri, Shahryar Rahnamayan, Masoud Makrehchi, Azam Asilian Bidgoli

“…The analysis was conducted on two cutting-edge DNN models: KimiaNet, a state-of-the-art DNN trained on TCGA images, and the self-trained EfficientNet. …”
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Model and Variable Selection Procedures for Semiparametric Time Series Regression by Risa Kato, Takayuki Shiohama

“…Semiparametric regression models are very useful for time series analysis. They facilitate the detection of features resulting from external interventions. …”
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ClearFinder: a Python GUI for annotating cells in cleared mouse brain by Stefan Pastore, Philipp Hillenbrand, Nils Molnar, Irina Kovlyagina, Monika Chanu Chongtham, Stanislav Sys, Beat Lutz, Margarita Tevosian, Susanne Gerber

“…Results We developed an easy-to-adopt graphical user interface for cell quantification and group analysis of whole cleared adult mouse brains. Fundamental statistical analysis, such as PCA and box plots, and additional visualization features allow for quick data evaluation and quality checks. …”
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A correlation study on EEG signals during visual concentration test and clinical evaluation in schizophrenia patients by Min-Wei Huang, Qun-Wei Chang, Wen-Lin Chu

“…Results The study successfully demonstrated the potential of a concentration detection system, integrating EEG signal analysis with PANSS scores, to classify schizophrenia severity accurately. …”
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ALGORITHM OF PREPARATION OF THE TRAINING SAMPLE USING 3D-FACE MODELING by D. I. Samal, I. I. Frolov

“…The preparation and preliminary processing of images contains the following constituents like detection and localization of area of the person on the image, assessment of an angle of rotation and an inclination, extension of the range of brightness of pixels and an equalization of the histogram to smooth the brightness and contrast characteristics of the processed images, scaling of the localized and processed area of the person, creation of a vector of features of the scaled and processed image of the person by a Principal component analysis (algorithm NIPALS), training of the multiclass SVM-classifier.The provided algorithm of expansion of the training selection is oriented to be used in practice and allows to expand using 3D-models the processed range of 2D – photographs of persons that positively affects results of identification in system of face recognition. …”
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HASSDE-NAS: Heuristic–Adaptive Spectral–Spatial Neural Architecture Search with Dynamic Cell Evolution for Hyperspectral Water Body Identification by Feng Chen, Baishun Su, Zongpu Jia

“…The accurate identification of water bodies in hyperspectral images (HSIs) remains challenging due to hierarchical representation imbalances in deep learning models, where shallow layers overly focus on spectral features, boundary ambiguities caused by the relatively low spatial resolution of satellite imagery, and limited detection capability for small-scale aquatic features such as narrow rivers. …”
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c-Triadem: A constrained, explainable deep learning model to identify novel biomarkers in Alzheimer's disease. by Sherlyn Jemimah, Ferial Abuhantash, Aamna AlShehhi

“…The model can be used for detection and biomarker identification in other related dementias.…”
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A Novel THz Differential Spectral Clustering Recognition Method Based on t-SNE by Tie-Jun Li, Chih-Cheng Chen, Jian-jun Liu, Gui-fang Shao, Christopher Chun Ki Chan

“…Comparative experiments indicate that our model can judge the existence of sample defect features or not through spectrum clustering, as a predetection process for image analysis.…”
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A Review on Fall Prediction and Prevention System for Personal Devices: Evaluation and Experimental Results by Masoud Hemmatpour, Renato Ferrero, Bartolomeo Montrucchio, Maurizio Rebaudengo

“…Results show that tilt features in combination with a decision tree algorithm present the best performance.…”
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Torsion-Driven Plectoneme Formation During Nanopore Translocation of DNA Polymers by Fei Zheng, Antonio Suma, Christopher Maffeo, Kaikai Chen, Mohammed Alawami, Jingjie Sha, Aleksei Aksimentiev, Cristian Micheletti, Ulrich F. Keyser

“…Thus, our work introduces plectonemes as essential structural features that must be considered for accurate analysis of dsDNA polymers in the nanopore.…”
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Cognitive and neuropsychiatric disorders in extrapyramidal diseases by O. S. Levin, M. A. Anikin, E. E. Vasenina

“…Analysis of the specific features of neuropsychological and behavioral disorders allows one to specify the localization of a pathological process and its extent, which may be of important differential diagnostic value. …”
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