DANNET: deep attention neural network for efficient ear identification in biometrics by Deepthy Mary Alex, Kalpana Chowdary M., Hanan Abdullah Mengash, Venkata Dasu M., Natalia Kryvinska, Chinna Babu J., Ajmeera Kiran

“…The use of an ensemble method is crucial in ear biometrics due to the variability and complexity of ear shapes and the potential for partial occlusions. …”
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Multiclass leukemia cell classification using hybrid deep learning and machine learning with CNN-based feature extraction by Sazzli Kasim, Sorayya Malek, JunJie Tang, Xue Ning Kiew, Song Cheen, Bryan Liew, Norashikin Saidon, Raja Ezman, Raja Shariff

“…Peripheral blood smear analysis, a key non-invasive diagnostic tool, often suffers from subjective interpretation, inter-observer variability, and a lack of readily available expertise. …”
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Automated Pipeline for Robust Cat Activity Detection Based on Deep Learning and Wearable Sensor Data by Md Ariful Islam Mozumder, Tagne Poupi Theodore Armand, Rashadul Islam Sumon, Shah Muhammad Imtiyaj Uddin, Hee-Cheol Kim

“…To estimate a cat’s behavior, objective observations of both the frequency and variability of specific behavior traits are required, which might be difficult to come by in a cat’s ordinary life. …”
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CART-ANOVA-Based Transfer Learning Approach for Seven Distinct Tumor Classification Schemes with Generalization Capability by Shiraz Afzal, Muhammad Rauf, Shahzad Ashraf, Shahrin Bin Md Ayob, Zeeshan Ahmad Arfeen

“…<b>Background/Objectives:</b> Deep transfer learning, leveraging convolutional neural networks (CNNs), has become a pivotal tool for brain tumor detection. …”
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Wheat Soil-Borne Mosaic Virus Disease Detection: A Perspective of Agricultural Decision-Making via Spectral Clustering and Multi-Indicator Feedback by Xue Hou, Chao Zhang, Yunsheng Song, Turki Alghamdi, Majed Aborokbah, Hui Zhang, Haoyue La, Yizhen Wang

“…Due to the regional variability in environmental conditions and symptom expressions, accurately evaluating the severity of wheat soil-borne mosaic (WSBM) infections remains a persistent challenge. …”
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Interpretable Machine Learning for Multi-Crop Yield Prediction in Semi-Arid Regions: A Hierarchical Approach to Handle Climate Data Sparsity by Rachid Ed-daoudi, M’barek El Haloui

“…Model interpretability is achieved through SHapley Additive exPlanations (SHAP) analysis and uncertainty decomposition, quantifying the contributions of data variability, temporal dynamics, and model ensembles. …”
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Advanced predictive machine and deep learning models for round-ended CFST column by Feng Shen, Ishan Jha, Haytham F. Isleem, Walaa J.K. Almoghayer, Mohammad Khishe, Mohamed Kamel Elshaarawy

“…Comparison with 10 analytical models demonstrates that these traditional methods, though deterministic, struggle to capture the nonlinear interactions inherent in CFST columns, thus yielding lower accuracy and higher variability. In contrast, the data-driven models presented here offer robust, adaptable, and interpretable solutions, underscoring their potential to transform design and analysis practices for CFST columns, ultimately fostering safer and more efficient structural systems.…”
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Artificial Intelligence-based Approaches for Characterizing Plaque Components From Intravascular Optical Coherence Tomography Imaging: Integration Into Clinical Decision Support Sy... by Michela Sperti, Camilla Cardaci, Francesco Bruno, Syed Taimoor Hussain Shah, Konstantinos Panagiotopoulos, Karim Kassem, Giuseppe De Nisco, Umberto Morbiducci, Raffaele Piccolo, Francesco Burzotta, Fabrizio D’Ascenzo, Marco Agostino Deriu, Claudio Chiastra

“…Manual plaque assessment by experts is time-consuming, prone to errors, and affected by high inter-observer variability. To increase productivity, precision, and reproducibility, researchers are increasingly integrating artificial intelligence (AI)-based techniques into IVOCT analysis pipelines. …”
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Enhancing Water Bodies Detection in the Highland and Coastal Zones Through Multisensor Spectral Data Fusion and Deep Learning by Xiaofei Han, Nazih Y. Rebouh, Yasmeen Ahmed, Muhammad Nasar Ahmad, Zainab Tahir, Yahia Said, Ishfaq Gujree

“…Accurate mapping of inland and coastal water bodies is crucial for monitoring environmental changes, managing hydrological resources, and assessing the impacts of climatic variability. This study presents a deep-learning-based semantic segmentation framework that leverages multiband Sentinel-2 imagery for delineating glaciers and coastal lakes. …”
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A Multi-Scale Deep Learning Framework Combining MobileViT-ECA and LSTM for Accurate ECG Analysis by Abduljabbar S. Ba Mahel, Mehdhar S. A. M. Al-Gaashani, Reem Ibrahim Alkanhel, Dina S. M. Hassan, Mohammed Saleh Ali Muthanna, Ammar Muthanna, Ahmed Aziz

“…Electrocardiogram (ECG) analysis is crucial for diagnosing cardiovascular diseases (CVD), especially atrial fibrillation (AF), a prevalent cardiac rhythm abnormality. However, the variability and complexity of ECG signals make AF classification challenging, highlighting the need for more accurate and reliable methods. …”
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Apple Yield Estimation Method Based on CBAM-ECA-Deeplabv3+ Image Segmentation and Multi-Source Feature Fusion by Wenhao Cui, Yubin Lan, Jingqian Li, Lei Yang, Qi Zhou, Guotao Han, Xiao Xiao, Jing Zhao, Yongliang Qiao

“…Apple yield estimation is a critical task in precision agriculture, challenged by complex tree canopy structures, growth stage variability, and orchard heterogeneity. In this study, we apply multi-source feature fusion by combining vegetation indices from UAV remote sensing imagery, structural feature ratios from ground-based fruit tree images, and leaf chlorophyll content (SPAD) to improve apple yield estimation accuracy. …”
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Functional connectivity in EEG: a multiclass classification approach for disorders of consciousness by Sreelakshmi Raveendran, Kala S, Ramakrishnan A G, Ramakrishnan A G, Raghavendra Kenchaiah, Jayakrushna Sahoo, Santhos Kumar, Farsana M K, Ravindranadh Chowdary Mundlamuri, Sonia Bansal, Binu V S, Subasree R

“…The extracted SWC metrics, mean, reflecting the stability of connectivity, and standard deviation, indicating variability, are analyzed to discern FC differences at the group level. …”
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Deep Learning with Transfer Learning on Digital Breast Tomosynthesis: A Radiomics-Based Model for Predicting Breast Cancer Risk by Francesca Galati, Roberto Maroncelli, Chiara De Nardo, Lucia Testa, Gloria Barcaroli, Veronica Rizzo, Giuliana Moffa, Federica Pediconi

“…<b>Background</b>: Digital breast tomosynthesis (DBT) is a valuable imaging modality for breast cancer detection; however, its interpretation remains time-consuming and subject to inter-reader variability. This study aimed to develop and evaluate two deep learning (DL) models based on transfer learning for the binary classification of breast lesions (benign vs. malignant) using DBT images to support clinical decision-making and risk stratification. …”
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AI in 2D Mammography: Improving Breast Cancer Screening Accuracy by Sebastian Ciurescu, Simona Cerbu, Ciprian Nicușor Dima, Florina Borozan, Raluca Pârvănescu, Diana-Gabriela Ilaș, Cosmin Cîtu, Corina Vernic, Ioan Sas

“…Two-dimensional (2D) mammography is the established standard for breast cancer screening; however, its diagnostic accuracy is limited by factors such as breast density and inter-reader variability. Recent advances in artificial intelligence (AI) have shown promise in enhancing radiological interpretation. …”
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Artificial Intelligence Models for Pediatric Lung Sound Analysis: Systematic Review and Meta-Analysis by Ji Soo Park, Sa-Yoon Park, Jae Won Moon, Kwangsoo Kim, Dong In Suh

“…Auscultation of lung sounds is a key diagnostic tool but is prone to subjective variability. The integration of artificial intelligence (AI) and machine learning (ML) with electronic stethoscopes offers a promising approach for automated and objective lung sound. …”
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An explainable AI-driven deep neural network for accurate breast cancer detection from histopathological and ultrasound images by Md. Romzan Alom, Fahmid Al Farid, Muhammad Aminur Rahaman, Anichur Rahman, Tanoy Debnath, Abu Saleh Musa Miah, Sarina Mansor

“…However, traditional diagnostic processes relying on manual analysis of medical images are inherently complex and subject to variability between observers, highlighting the urgent need for robust automated breast cancer detection systems. …”
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Artificial Intelligence (AI) approach for the quantification of C-phycocyanin in Spirulina platensis: Hybrid stacking-ensemble model based on machine learning and deep learning by Jun Wei Roy Chong, Kuan Shiong Khoo, Huong-Yong Ting, Iwamoto Koji, Zengling Ma, Pau Loke Show

“…This study proposes a hybrid stacking-ensemble model integrating convolutional neural networks (CNN) for automated feature extraction with both Support Vector Machine (SVM) and eXtreme gradient boosting (XGBoost) as base models and multiple meta-regressor models. …”
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Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female b... by Ricardo Gonzalez, Peyman Nejat, Ashirbani Saha, Clinton J.V. Campbell, Andrew P. Norgan, Cynthia Lokker

“…Most studies used Convolutional Neural Networks and one used logistic regression algorithms. …”
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Deep Learning and Edge Computing in Agriculture: A Comprehensive Review of Recent Trends and Innovations by Apri Junaidi, Siti Zaiton Mohd Hashim, Mohd Shahizan Bin Othman, Mohd Murtadha Bin Mohamad, Hitham Alhussian, Said Jadid Abdulkadir, Maged Nasser, Yunusa Adamu Bena

“…Early and accurate detection of such diseases is critical to minimizing crop loss, particularly under conditions of labor shortages and climate variability. Traditional inspection methods are labor-intensive and error-prone, highlighting the need for automated, intelligent solutions. …”
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A multimodal deep learning architecture for predicting interstitial glucose for effective type 2 diabetes management by Muhammad Salman Haleem, Daphne Katsarou, Eleni I. Georga, George E. Dafoulas, Alexandra Bargiota, Laura Lopez-Perez, Miguel Rujas, Giuseppe Fico, Leandro Pecchia, Dimitrios Fotiadis, Gatekeeper Consortium

“…However, a key challenge in the effective management of type 2 diabetes lies in forecasting critical events driven by glucose variability. While recent advances in deep learning enable modeling of temporal patterns in glucose fluctuations, most of the existing methods rely on unimodal inputs and fail to account for individual physiological differences that influence interstitial glucose dynamics. …”
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