Using social media for environmental insight: a multi-model deep learning framework approach by Minglu Che, Chengyao Wang, Yanyun Nian, Pinqi Rao

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A Radiomic-based model to predict the depth of myometrial invasion in endometrial cancer on ultrasound images by Francesca Arezzo, Annarita Fanizzi, Rosanna Mancari, Emiliano Cocco, Samantha Bove, Maria Colomba Comes, Mariangela Gianciotta, Giorgia Lanza, Salvatore Lopez, Gerardo Cazzato, Erica Silvestris, Elsa Vitale, Enrico Vizza, Gennaro Cormio, Raffaella Massafra, Vera Loizzi

“…The binary and multiclass model reached optimal classification performances with a mean AUC value equals to 90.76 (88.63–92.89 IC95%). …”
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Enhancing EEG-Based Emotion Detection with Hybrid Models: Insights from DEAP Dataset Applications by Badr Mouazen, Ayoub Benali, Nouh Taha Chebchoub, El Hassan Abdelwahed, Giovanni De Marco

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Decoding vocal indicators of stress in laying hens: A CNN-MFCC deep learning framework by Suresh Neethirajan

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Data Analytics and Machine Learning Models on COVID-19 Medical Reports Enhanced with XAI for Usability by Oliver Lohaj, Ján Paralič, Zuzana Paraličová, Daniela Javorská, Elena Zagorová

“…The best model for predicting mortality risk was also the LightGBM model, which achieved a ROC AUC score of 83.7% and a classification accuracy of 81.2% using all features. …”
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T_SRNET: A multimodal model based on convolutional neural network for emotional speech enhancement by Shaoqiang Wang, Lei Feng, Li Zhang

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Smart sustainable cyber security: modelling an interpretable and transparent threat detection with explainable artificial intelligence by Saif Jasim Almheiri, Asghar Ali Shah, Sagheer Abbas, Munir Ahmad, Muhammad Adnan Khan

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Predictive modeling of pediatric drug-induced liver injury: Dynamic classifier selection with clustering analysis by Zixin Shi, Linjun Huang, Haolin Wang

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IM- LTS: An Integrated Model for Lung Tumor Segmentation using Neural Networks and IoMT by Jayapradha J, Su-Cheng Haw, Naveen Palanichamy, Kok-Why Ng, Senthil Kumar Thillaigovindhan

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How accurately can supervised machine learning model predict a targeted psychiatric disorder? by Haitham Jahrami, Amir H. Pakpour, Waqar Husain, Achraf Ammar, Zahra Saif, Ali Husain Alsalman, Adel Aloffi, Khaled Trabelsi, Seithikurippu R. Pandi-Perumal, Michael V. Vitiello

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Radiomics-driven neuro-fuzzy framework for rule generation to enhance explainability in MRI-based brain tumor segmentation by Leondry Mayeta-Revilla, Leondry Mayeta-Revilla, Leondry Mayeta-Revilla, Leondry Mayeta-Revilla, Eduardo P. Cavieres, Eduardo P. Cavieres, Eduardo P. Cavieres, Matías Salinas, Matías Salinas, Matías Salinas, Diego Mellado, Diego Mellado, Diego Mellado, Diego Mellado, Sebastian Ponce, Sebastian Ponce, Sebastian Ponce, Sebastian Ponce, Francisco Torres Moyano, Francisco Torres Moyano, Francisco Torres Moyano, Francisco Torres Moyano, Steren Chabert, Steren Chabert, Steren Chabert, Marvin Querales, Marvin Querales, Julio Sotelo, Rodrigo Salas, Rodrigo Salas, Rodrigo Salas

“…Although Deep Learning (DL) models offer strong performance in tumor detection and segmentation using MRI, their black-box nature hinders clinical adoption due to a lack of interpretability.MethodsWe present a hybrid AI framework that integrates a 3D U-Net Convolutional Neural Network for MRI-based tumor segmentation with radiomic feature extraction. …”
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Predicting depression risk in COPD patients: a model based on NHANES 2007–2012 data by Jinyan Zhang, Shenghong Zhou

“…Subsequently, univariate and multivariate logistic regression analyses were conducted to determine independent predictors for constructing the nomogram. The model was then evaluated using the C-index, calibration curve, Hosmer–Lemeshow test, and decision curve analysis (DCA). …”
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Development of an explainable machine learning model for Alzheimer’s disease prediction using clinical and behavioural features by Rajkumar Govindarajan, K. Thirunadanasikamani, Komal Kumar Napa, S. Sathya, J. Senthil Murugan, K. G. Chandi Priya

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Enhanced brain tumor diagnosis using combined deep learning models and weight selection technique by Karim Gasmi, Najib Ben Aoun, Najib Ben Aoun, Khalaf Alsalem, Ibtihel Ben Ltaifa, Ibrahim Alrashdi, Lassaad Ben Ammar, Manel Mrabet, Abdulaziz Shehab

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Reinforcement Q-Learning-Based Adaptive Encryption Model for Cyberthreat Mitigation in Wireless Sensor Networks by Sreeja Balachandran Nair Premakumari, Gopikrishnan Sundaram, Marco Rivera, Patrick Wheeler, Ricardo E. Pérez Guzmán

“…This paper proposes a reinforcement learning-based adaptive encryption framework that dynamically scales encryption levels based on real-time network conditions and threat classification. The proposed model leverages a deep learning-based anomaly detection system to classify network states into low, moderate, or high threat levels, which guides encryption policy selection. …”
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Development and validation of a risk prediction model for diabetic kidney disease in patients with diabetic retinopathy by Mengsha Yin, Wenke Dong, Linan Ren, Mingyue Han, Guixia Wang, Yao Wang, Xiaokun Gang

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Predictive Modeling for Fetal Health: A Comparative Study of PCA, LDA, and KPCA for Dimensionality Reduction by Ariana Deyaneira Jimenez-Narvaez, Victor David Casa Vaca, Jonathan Javier Loor-Duque, Isidro Rafael Amaro Martin, Ivan Galo Reyes-Chacon, Paulina Vizcaino, Manuel Eugenio Morocho-Cayamcela

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Information-theoretic bounds for accuracy of letter encoding and pattern recognition via ensembles of datasets M.M. Lange 1, A.M. Lange 1 by M.M. Lange, A.M. Lange

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Gas Pipeline Leak Detection by Integrating Dynamic Modeling and Machine Learning Under the Transient State by Juhyun Kim, Sunlee Han, Daehee Kim, Youngsoo Lee

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Research on the Construction and Application of Earthquake Emergency Information Knowledge Graph Based on Large Language Models by Wentao Zhou, Meng Huang, Shuai Liu, Qiao You, Fanxin Meng

“…To address the challenges of semantic parsing of multi-source heterogeneous information and the delayed emergency response decisions caused by insufficient relational reasoning capabilities in earthquake emergency management, this study proposes a domain knowledge extraction method for earthquakes based on a large language model combined with a three-level prompt engineering system (TPES-LLM) of “instruction fine-tuning - demand awareness - case matching. …”
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