Opportunities and practices supporting responsive health care for forced migrants: lessons from transnational practice and a mixed-methods systematic review by Amy Robinson, Protus Musotsi, Ziaur Rahman A Khan, Laura Nellums, Bayan Faiq, Kofi Broadhurst, Gisela Renolds, Michael Pritchard, Andrew Smith

“…We reviewed service information and evaluations where available. Qualitative evidence, including field notes and documents, was combined in a broad thematic analysis. …”
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A hybrid framework of generative deep learning for antiviral peptide discovery by Huynh Anh Duy, Tarapong Srisongkram

“…In this study, we present a hybrid model combining Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) and Bidirectional Long Short-Term Memory (BiLSTM) networks to address these challenges. …”
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Hybrid LSTM–Attention and CNN Model for Enhanced Speech Emotion Recognition by Fazliddin Makhmudov, Alpamis Kutlimuratov, Young-Im Cho

“…In this research, we present a comprehensive speech emotion-recognition framework that amalgamates the ZCR, RMS, and MFCC feature sets. Our approach employs both CNN and LSTM networks, complemented by an attention model, for enhanced emotion prediction. …”
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Deep Learning Based Visual Servo for Autonomous Aircraft Refueling by Natthaphop Phatthamolrat, Teerawat Tongloy, Siridech Boonsang, Santhad Chuwongin

“…The proposed method integrates two‐stage neural networks with the Efficient Perspective‐n‐Point (EPnP) principle to determine the orientation and rotation angles, while an approximation principle based on feature point errors calculates linear positions. …”
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UAV-Satellite Cross-View Image Matching Based on Adaptive Threshold-Guided Ring Partitioning Framework by Yushi Liao, Juan Su, Decao Ma, Chao Niu

“…The framework synergizes three core components: brightness-aligned preprocessing to reduce illumination-induced domain shifts, hybrid loss functions to improve feature discriminability across domains, and keypoint-aware re-ranking to refine retrieval results by compensating for neural networks’ localization uncertainty. …”
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Neuro-Visual Adaptive Control for Precision in Robot-Assisted Surgery by Claudio Urrea, Yainet Garcia-Garcia, John Kern, Reinier Rodriguez-Guillen

“…Performance evaluations were conducted in CoppeliaSim, utilizing real cholecystectomy images featuring surgical tools. …”
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A comprehensive case study of deep learning on the detection of alpha thalassemia and beta thalassemia using public and private datasets by Muhammad Umar Nasir, Muhammad Tahir Naseem, Taher M. Ghazal, Muhammad Zubair, Oualid Ali, Sagheer Abbas, Munir Ahmad, Khan Muhammad Adnan

“…Public datasets were sourced from medical databases, while private datasets were collected from clinical records, offering a more comprehensive feature set and larger sample sizes. After data preprocessing and splitting, model performance was evaluated. …”
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Multimodal GeoAI: An integrated spatio-temporal topic-sentiment model for the analysis of geo-social media posts for disaster management by David Hanny, Bernd Resch

“…The analysis of online communication on social networks has become a central research interest to improve disaster management. …”
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Neurophysiological Approaches to Lie Detection: A Systematic Review by Bewar Neamat Taha, Muhammet Baykara, Talha Burak Alakuş

“…This systematic review aims to evaluate recent studies (2017–2024) on EEG-based lie detection using ERP P300 responses, especially in relation to recognized and unrecognized face stimuli. …”
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Hybrid weighted fuzzy production rule extraction utilizing modified harmony search and BPNN by Feng Qin, Azlan Mohd Zain, Kai-Qing Zhou, De-Bing Zhuo

“…This paper proposes an integrated approach combining the Dynamic Dimension Adjustment Harmony Search (DDA-HS) Algorithm and Back Propagation Neural Networks (BPNNs) to enhance WFPR extraction accuracy. …”
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Variation within and between digital pathology and light microscopy for the diagnosis of histopathology slides: blinded crossover comparison study by David RJ Snead, Ayesha S Azam, Jenny Thirlwall, Peter Kimani, Louise Hiller, Adam Bickers, Clinton Boyd, David Boyle, David Clark, Ian Ellis, Kishore Gopalakrishnan, Mohammad Ilyas, Paul Kelly, Maurice Loughrey, Desley Neil, Emad Rakha, Ian SD Roberts, Shatrughan Sah, Maria Soares, YeeWah Tsang, Manuel Salto-Tellez, Helen Higgins, Donna Howe, Abigail Takyi, Yan Chen, Agnieszka Ignatowicz, Jason Madan, Henry Nwankwo, George Partridge, Janet Dunn

“…Experienced pathologists were quicker to recognise features and move on than less experienced pathologists. …”
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Analysis of food policymaking through a food systems lens: a review of analytical frameworks by Isobel Stanley, Celine Murrin

“…Identified frameworks were evaluated using theories of systems approaches to food policy development. …”
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A machine learning framework for predicting healthcare utilization and risk factors by Yead Rahman, Prerna Dua

“…Seven supervised models, i.e., logistic regression, extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), random forest, decision tree, artificial neural networks (ANN), and naïve bayes, were evaluated based on predictive performance, computational efficiency, and feature importance. …”
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Estimation the validity of remote sensed rainfall data over selected regions in Iraq by Zahraa Al-Montaser

“…Existing rain gauge networks lack the temporal and geographical coverage required for adequate monitoring. …”
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Machine Learning-Based Prediction Performance Comparison of Marshall Stability and Flow in Asphalt Mixtures by Muhammad Farhan Zahoor, Arshad Hussain, Afaq Khattak

“…Their performances vary; therefore, evaluating different MLAs and comparing them is important. …”
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A Hybrid Deep Learning-Machine Learning Stacking Model for Yemeni Arabic Dialect Sentiment Analysis by Alaa Abdulkareem Hameed Brihi, Mossa Ghurab

“…The experimental results indicate a hybrid approach that combines advanced deep learning models with a stacked approach, leveraging deep networks for hierarchical feature extraction and RF/LinearSVC as meta-learners, achieving the highest accuracies of 94.71% and 94.28%, respectively. …”
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Increasing the Robustness of Image Quality Assessment Models Through Adversarial Training by Anna Chistyakova, Anastasia Antsiferova, Maksim Khrebtov, Sergey Lavrushkin, Konstantin Arkhipenko, Dmitriy Vatolin, Denis Turdakov

“…Adversarial training has been widely used to improve the robustness of neural networks to adversarial attacks, but little in-depth research has examined adversarial training as a way to improve IQA model robustness. …”
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Stratified Multisource Optical Coherence Tomography Integration and Cross-Pathology Validation Framework for Automated Retinal Diagnostics by Michael Sher, Riah Sharma, David Remyes, Daniel Nasef, Demarcus Nasef, Milan Toma

“…The methodology integrates biomimetic data partitioning, deep biomarker extraction via pretrained VGG16 networks, and automated model selection optimized for clinical decision-making. …”
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Machine Learning-Based Prediction of Postoperative Deep Vein Thrombosis Following Tibial Fracture Surgery by Humam Baki, İsmail Bülent Özçelik

“…A total of 42 predictive models were developed using combinations of six ML algorithms—logistic regression, support vector machine, random forest, extreme gradient boosting, Light Gradient Boosting Machine (LightGBM), and neural networks—and seven feature selection methods, including SHapley Additive exPlanations (SHAP), Least Absolute Shrinkage and Selection Operator (LASSO), Boruta, recursive feature elimination, univariate filtering, and full-variable inclusion. …”
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Hybrid Backbone-Based Deep Learning Model for Early Detection of Forest Fire Smoke by Gökalp Çınarer

“…This study proposes a novel deep learning-based approach that significantly enhances the accuracy of fire detection by incorporating advanced feature extraction techniques. Through rigorous experiments and comprehensive evaluations, our method outperforms existing approaches, demonstrating its effectiveness in detecting fires at an early stage. …”
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