Remaining Useful Life Estimation of Used Li-Ion Cells With Deep Learning Algorithms Without First Life Information by I. Sanz-Gorrachategui, Y. Wang, A. Guillen-Asensio, A. Bono-Nuez, B. Martin-del-Brio, P. V. Orlik, P. Pastor-Flores

“…These features are automatically processed by deep learning algorithms, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. …”
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Identification of right ventricular dysfunction with LogNNet based diagnostic model: A comparative study with supervised ML algorithms by Mehmet Tahir Huyut, Andrei Velichko, Maksim Belyaev, Yuriy Izotov, Şebnem Karaoğlanoğlu, Bünyamin Sertoğullarından, Sıddık Keskin, Dmitry Korzun

“…Additionally, combinations of these features demonstrated high predictive power. LogNNet achieved robust results with only a few selected features, making it suitable for applications in resource-limited environments. …”
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Improved UWB-based indoor positioning system via NLOS classification and error mitigation by Shoude Wang, Nur Syazreen Ahmad

“…To address this issue, we propose a method for identifying and classifying NLOS signals based on Support Vector Machine Recursive Feature Elimination (SVM-RFE). We extract multiple features from the UWB Channel Impulse Response (CIR) and perform correlation analysis using the Pearson Correlation Coefficient (PCC) to select the most discriminative features via the SVM-RFE algorithm. …”
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Multimodal Explainability Using Class Activation Maps and Canonical Correlation for MI-EEG Deep Learning Classification by Marcos Loaiza-Arias, Andrés Marino Álvarez-Meza, David Cárdenas-Peña, Álvaro Ángel Orozco-Gutierrez, German Castellanos-Dominguez

“…Our approach involves the following: (i) evaluating different deep learning (DL) models for subject-dependent MI-EEG discrimination; (ii) employing class activation mapping (CAM) to visualize relevant MI-EEG features; and (iii) utilizing a questionnaire–MI performance canonical correlation analysis (QMIP-CCA) to provide multidomain interpretability. …”
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Machine Learning Approaches to Predict No-Shows in Saudi Arabian Primary and General Healthcare Settings by Abdulrahman Alshehri, Abdullah Saeed, Abdullah AlShafea, Sabah Althubiany, Mohammed Alshehri, Amer Alzahrani, Khalid Hakami, Lamia Ibrahim, Abdulrahim Alshehri, Rana Alamri

“…Machine learning models, such as decision trees, random forests, Naive Bayes, logistic regression, and artificial neural networks (ANN), have been developed and evaluated based on accuracy, precision, recall, F1 score, and area under the curve (AUC). …”
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ACU-Net: Attention-based convolutional U-Net model for segmenting brain tumors in fMRI images by Md Alamin Talukder, Md Abu Layek, Md Aslam Hossain, Md Aminul Islam, Mohammad Nur-e-Alam, Mohsin Kazi

“…This study introduces the attention-based convolutional U-Net (ACU-Net) model, designed to improve segmentation accuracy and efficiency in fMRI images by incorporating attention mechanisms that selectively highlight critical features while preserving spatial context. Methods The ACU-Net model combines convolutional neural networks (CNNs) with attention mechanisms to enhance feature extraction and spatial coherence. …”
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Bayesian Model Prediction for Breast Cancer Survival: A Retrospective Analysis by Islam Bani Mohammad, Muayyad M. Ahmad

“…The data were randomly split into a training set (2,097 cases, 70%) and a test set (898 cases, 30%) for developing the Bayesian network model and predicting the overall survival of patients diagnosed with breast cancer. …”
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Building electrical consumption patterns forecasting based on a novel hybrid deep learning model by Nasser Shahsavari-Pour, Azim Heydari, Farshid Keynia, Afef Fekih, Aylar Shahsavari-Pour

“…Specifically, the proposed model comprises three key components: (i) a mutual information-based feature selection method to identify the most significant input variables influencing energy consumption; (ii) a variational mode decomposition (VMD) approach to decompose the original energy consumption signal into intrinsic mode functions (IMFs), capturing relevant trends and eliminating noise; and (iii) a long short-term memory (LSTM) neural network to perform time-series forecasting of the target energy consumption values. …”
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Machine learning based insights into cardiomyopathy and heart failure research: a bibliometric analysis from 2005 to 2024 by Muhammad Junaid Akram, Muhammad Junaid Akram, Asad Nawaz, Asad Nawaz, Yuan Yuxing, Yuan Yuxing, Jinpeng Zhang, Jinpeng Zhang, Huang Haixin, Huang Haixin, Lingjuan Liu, Lingjuan Liu, Xu Qian, Jie Tian, Jie Tian

“…Global collaboration networks underscored strong partnerships but highlighted disparities in contributions from low-income regions.ConclusionThis analysis highlights the dynamic evolution of cardiomyopathy research, emphasizing the critical role of ML and AI in advancing diagnostics and therapeutic strategies. …”
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EXPLORING A NOVEL HEXACO PERSONALITY TRAITS ON TWITTER: AN ENSEMBLE-BASED NLP METHODOLOGY by Tanvi Desai, Divyakant Meva

“…Our approach integrates advanced NLP techniques across key phases: preprocessing, feature extraction, feature selection, and final detection. …”
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Identifying determinants of malnutrition in under-five children in Bangladesh: insights from the BDHS-2022 cross-sectional study by Tanzila Tamanna, Shohel Mahmud, Nahid Salma, Md. Musharraf Hossain, Md. Rezaul Karim

“…Boruta algorithm was employed to identify important features related to malnutrition which were then used to evaluate several machine learning models, including K-Nearest Neighbors (KNN), Neural Networks (NN), Classification and Regression Trees (CART), XGBoost (XGBM), Support Vector Machines (SVM), and Random Forest (RF), in addition to the traditional logistic regression (LR) model. …”
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Unsupervised Deep Clustering on Spatiotemporal Objects Extracted from 4D Point Clouds for Automatic Identification of Topographic Processes in Natural Environments by J. Wang, K. Anders

“…In this paper, we present a time series-based unsupervised deep clustering framework for identifying topographic processes without manual feature engineering and annotations. By leveraging the representation learning capability of autoencoders, especially using convolutional neural networks (CNNs) as feature extractors, our approach implements the dimensionality reduction of the original inputs to uniform low-dimensional vectors in latent space. …”
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Deep Learning-Based Detection of Tuberculosis Using a Gaussian Chest X-Ray Image Filter as a Software Lens by Luca Eisentraut, Christopher Mai, Johanna Hosch, Amelie Benecke, Pascal Penava, Ricardo Buettner

“…We propose that optimal detection performance may not rely on more complex architectures but instead on optimizing preprocessing techniques to highlight these features. Specifically, a ResNet50-based architecture with Gaussian filtering was evaluated on a dataset of 7,000 images using stratified 5-fold cross-validation. …”
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Pour une étude interdisciplinaire et sémio-linguistique de la viralité dans les médias by Alain Rabatel

“…When they emerge on a shared common background, all these parameters prove to be good predictors of viral dynamics on socio-numerical networks.…”
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Sample ascertainment and clinical outcome measures in the Accelerating Medicines Partnership® Schizophrenia Program by Jean Addington, Lu Liu, Amy Braun, Andrea Auther, Monica E. Calkins, Barbara A. Cornblatt, Cheryl M. Corcoran, Paolo Fusar-Poli, Melissa J. Kerr, Catalina V. Mourgues-Codern, Angela R. Nunez, Dominic Oliver, Gregory P. Strauss, Barbara C. Walsh, Luis K. Alameda, Celso Arango, Nicholas J. K. Breitborde, Matthew R. Broome, Kristin S. Cadenhead, Ricardo E. Carrion, Eric Yu Hai Chen, Jimmy Choi, Michael J. Coleman, Philippe Conus, Covadonga M. Diaz-Caneja, Dominic Dwyer, Lauren M. Ellman, Masoomeh Faghankhani, Pablo A. Gaspar, Carla Gerber, Louise Birkedal Glenthøj, Leslie E. Horton, Christy Hui, Grace R. Jacobs, Joseph Kambeitz, Lana Kambeitz-Ilankovic, Matcheri S. Keshavan, Sung-Wan Kim, Nikolaos Koutsouleris, Jun Soo Kwon, Kerstin Langbein, Kathryn E. Lewandowski, Daniel Mamah, Patricia J. Marcy, Daniel H. Mathalon, Vijay A. Mittal, Merete Nordentoft, Godfrey D. Pearlson, Nora Penzel, Jesus Perez, Diana O. Perkins, Albert R. Powers, Jack Rogers, Fred W. Sabb, Jason Schiffman, Jai L. Shah, Steven M. Silverstein, Stefan Smesny, William S. Stone, Andrew Thompson, Judy L. Thompson, Rachel Upthegrove, Swapna Verma, Jijun Wang, Heather M. Wastler, Alana Wickham, Inge Winter-van Rossum, Daniel H. Wolf, Sylvain Bouix, Ofer Pasternak, Rene S. Kahn, Carrie E. Bearden, John M. Kane, Patrick D. McGorry, Kate Buccilli, Barnaby Nelson, Martha E. Shenton, Scott W. Woods, the Accelerating Medicines Partnership® -Schizophrenia, Alison R. Yung

“…Abstract Clinical ascertainment and clinical outcome are key features of any large multisite study. In the ProNET and PRESCIENT research networks, the Accelerating Medicines Partnership® Schizophrenia (AMP®SCZ) Clinical Ascertainment and Outcome Measures Team aimed to establish a harmonized clinical assessment protocol across these two research networks and to define ascertainment criteria and primary and secondary endpoints. …”
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Toward Real-Time Recognition of Continuous Indian Sign Language: A Multi-Modal Approach Using RGB and Pose by M. Geetha, Neena Aloysius, Darshik A. Somasundaran, Amritha Raghunath, Prema Nedungadi

“…To address these challenges, we present SignFlow, a network for real-time recognition of continuous sign gestures. …”
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Identification of glass eel capture equipment in the Yangtze River estuary based on high-spatial -resolution imagery and an improved YOLOv8 model by Pengfei Zhu, Weifeng Zhou

“…To avoid the false detection of small targets, we introduce the asymptotic feature pyramid network to replace the original detection head, and add a detection layer for small targets, which improves the accuracy but increases the parameters and computation volume. …”
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Effective Characterization of Fractured Media With PEDL: A Deep Learning‐Based Data Assimilation Approach by Tongchao Nan, Jiangjiang Zhang, Yifan Xie, Chenglong Cao, Jichun Wu, Chunhui Lu

“…In this study, we formulate a novel DA approach known as parameter estimator with deep learning (PEDL) that harnesses the capabilities of DL to capture nonlinear relationships and extract non‐Gaussian features. To evaluate PEDL's performance, we conduct three case studies, comprising two numerical cases and one real‐world case. …”
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Advancing Self-Supervised Learning for Building Change Detection and Damage Assessment: Unified Denoising Autoencoder and Contrastive Learning Framework by Songxi Yang, Bo Peng, Tang Sui, Meiliu Wu, Qunying Huang

“…The proposed architecture integrates a dual denoising autoencoder with a Vision Transformer backbone and contrastive learning strategy, complemented by a Feature Pyramid Network-ResNet dual decoder and an Edge Guidance Module. …”
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Advancing breast cancer diagnosis: token vision transformers for faster and accurate classification of histopathology images by Mouhamed Laid Abimouloud, Khaled Bensid, Mohamed Elleuch, Mohamed Ben Ammar, Monji Kherallah

“…Second, the TokenLearner model extracts relevant regions from the selected input patches, tokenizes them to improve feature extraction, and trains all tokenized patches in an encoder transformer network. …”
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