Leveraging Large Language Models for Integrated Satellite-Aerial-Terrestrial Networks: Recent Advances and Future Directions by Shumaila Javaid, Ruhul Amin Khalil, Nasir Saeed, Bin He, Mohamed-Slim Alouini

“…This paper explores the transformative potential of integrating Large Language Models (LLMs) into ISATNs, leveraging advanced Artificial Intelligence (AI) and Machine Learning (ML) capabilities to enhance these networks. …”
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Activity prediction of anti-cancer drug candidate ERα inhibitor by XIA Yulan, XIE Jiming, WANG Yajing, LU Mengyuan, WANG Jinrui, QIN Yaqin

“…Then, a CNN-based two-dimensional feature matrix is constructed, and a Bayesian hyperparametric optimization (BHO) method is used to perform hyperparametric optimization of the Bi-LSTM model. …”
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Soil Organic Carbon Prediction and Mapping in Morocco Using PRISMA Hyperspectral Imagery and Meta-Learner Model by Yassine Bouslihim, Abdelkrim Bouasria, Budiman Minasny, Fabio Castaldi, Andree Mentho Nenkam, Ali El Battay, Abdelghani Chehbouni

“…This study presents a novel meta-learner framework that combines multiple machine learning algorithms and spectra processing algorithms to optimize SOC prediction using the PRISMA hyperspectral satellite imagery in the Doukkala plain of Morocco. …”
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Toward improving precision and complexity of transformer-based cost-sensitive learning models for plant disease detection by Manh-Tuan Do, Manh-Hung Ha, Duc-Chinh Nguyen, Oscal Tzyh-Chiang Chen, Oscal Tzyh-Chiang Chen

“…To optimize a model to a lightweight version, we integrated the proposed transformer model with the Ghost module. …”
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A New Classification Model Using a Decision Tree Generated from Hyperplanes in Dimensional Space by Benjamín Luna-Benoso, José Cruz Martínez-Perales, Úrsula Samantha Morales-Rodríguez, Rolando Flores-Carapia, Víctor Manuel Silva-García

“…However, one of the implications of the No-Free-Lunch theorems is that there is no optimal general-purpose model, i.e. there is no classifier model that achieves the best results for all problems presented. …”
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SiMBA-Augmented Physics-Informed Neural Networks for Industrial Remaining Useful Life Prediction by Min Li, Jianfeng Qin, Haifeng Fan, Ting Ke

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Soft sensor modeling method for Pichia pastoris fermentation process based on substructure domain transfer learning by Bo Wang, Jun Wei, Le Zhang, Hui Jiang, Cheng Jin, Shaowen Huang

“…Finally, based on the source and target domain data after substructure domain adaptation, the least squares support vector machine algorithm is used to establish the prediction model. …”
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The performance of the LSTM-based code generated by Large Language Models (LLMs) in forecasting time series data by Saroj Gopali, Sima Siami-Namini, Faranak Abri, Akbar Siami Namin

“…While the use of these game changing technologies in generating textual information has already been demonstrated in several application domains, their abilities in generating complex models and executable codes need to be explored. As an intriguing case is the goodness of the machine and deep learning models generated by these LLMs in conducting automated scientific data analysis, where a data analyst may not have enough expertise in manually coding and optimizing complex deep learning models and codes and thus may opt to leverage LLMs to generate the required models. …”
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STUDY OF PREDICTION AND CLASSIFICATION MODELS IN THE PROBLEMS OF DIABETES AMONG PATIENTS WITH A STROKE IN DIFFERENT LIVING CONDITIONS by Nural Huliiev, Maksym Peretiaha, Artem Khovrat, Denys Teslenko, Alexei Nazarov

“…Results: the initial conditions for choosing the best model are met by logistic regression. Conclusions: as a result of the study, the optimal model for predicting the development of the disease was selected. …”
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AI-powered topic modeling: comparing LDA and BERTopic in analyzing opioid-related cardiovascular risks in women by Li Ma, Li Ma, Ru Chen, Weigong Ge, Paul Rogers, Beverly Lyn-Cook, Huixiao Hong, Weida Tong, Ningning Wu, Wen Zou

“…Topic modeling is a crucial technique in natural language processing (NLP), enabling the extraction of latent themes from large text corpora. …”
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Construction and application of a drought classification model for tea plantations based on multi-source remote sensing by Yang Xu, Yilin Mao, He Li, Xiaojiang Li, Litao Sun, Kai Fan, Zhipeng Li, Shuting Gong, Zhaotang Ding, Yu Wang

“…A UAV platform equipped with MS, RGB, and TIR sensors collected multi-source data from drought-stressed tea plantations in Eastern China. The RSDCM model was benchmarked against single BP and three classical machine learning models (SVM, RF, ELM).The study found that: (1) Multi-source data fusion outperformed single-source data, with MS + TIR achieving optimal performance (Accuracy: 0.983, Precision: 0.967-1.000, Recall: 0.967-1.000, F1-score: 0.967-1.000)—surpassing MS (Accuracy: 0.950, Precision: 0.894-1.000, Recall: 0.917-0.983, F1-score: 0.924-0.983), TIR (Accuracy: 0.925, Precision: 0.862-0.982, Recall: 0.867-0.983, F1-score: 0.889-0.967), and RGB (Accuracy: 0.904, Precision: 0.824-0.950, Recall: 0.783-0.950, F1-score: 0.847-0.950) alone. (2) The RSDCM model (accuracy: 0.983) performed better than the other four models, with high generalizability across all drought levels (F1-scores: 0.967–1.000 for severe/moderate/light/normal classes). (3) The RSDCM model could accurately classify drought stress levels in tea plantations.Thus, RSDCM provides a novel, robust solution for UAV-based drought assessment in tea plantations, combining multi-sensor fusion and deep learning.…”
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AI models for the identification of prognostic and predictive biomarkers in lung cancer: a systematic review and meta-analysis by Hind M. AlOsaimi, Aseel M. Alshilash, Layan K. Al-Saif, Jannat M. Bosbait, Roaa S. Albeladi, Dalal R. Almutairi, Alwaleed A. Alhazzaa, Tariq A. Alluqmani, Saud M. Al Qahtani, Sara A. Almohammadi, Razan A. Alamri, Abdullah A. Alkurdi, Waleed K. Aljohani, Raghad H. Alraddadi, Mohammed K. Alshammari

“…Data extraction, quality assessment, and meta-analysis were performed according to PRISMA guidelines.ResultsA total of 34 studies met the inclusion criteria, encompassing diverse AI methodologies and biomarker targets. AI models, particularly deep learning and machine learning algorithms demonstrated high accuracy in predicting biomarker status. …”
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Fine-tuned YOLO-based deep learning model for detecting malaria parasites and leukocytes in thick smear images: A Tanzanian case study by Beston Lufyagila, Bonny Mgawe, Anael Sam

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Conventional and futuristic approaches for the computation of groundwater recharge: A comprehensive review by Shamla Rasheed, Marykutty Abraham

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Development of Quantitative Structure–Anti-Inflammatory Relationships of Alkaloids by Cristian Rojas, Doménica Muñoz, Ivanna Cordero, Belén Tenesaca, Davide Ballabio

“…During the calibration of the models, a five-fold Venetian blinds cross-validation was used to optimize the classifier parameters and to control the presence of overfitting. …”
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High-Resolution Mapping of Litter and Duff Fuel Loads Using Multispectral Data and Random Forest Modeling by Álvaro Agustín Chávez-Durán, Miguel Olvera-Vargas, Inmaculada Aguado, Blanca Lorena Figueroa-Rangel, Ramón Trucíos-Caciano, Ernesto Alonso Rubio-Camacho, Jaqueline Xelhuantzi-Carmona, Mariano García

“…A set of vegetation indices and texture metrics derived from the multispectral data, optimized by a “Variable Selection Using Random Forests” (VSURF) algorithm, were used to train random forest (RF) models, enabling the modeling of high-resolution maps of litter and duff fuel loads. …”
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A novel, rapid, and practical prognostic model for sepsis patients based on dysregulated immune cell lactylation by Chang Li, Mei He, PeiChi Shi, Lu Yao, XiangZhi Fang, XueFeng Li, QiLan Li, XiaoBo Yang, JiQian Xu, You Shang, You Shang

“…The proposed individualized prognostic model, based on dysregulated immune cell metabolism, accurately predicts early mortality and may inform optimized clinical management of septic patients.…”
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Enhancing Anomaly Detection Through Latent Space Manipulation in Autoencoders: A Comparative Analysis by Tomasz Walczyna, Damian Jankowski, Zbigniew Piotrowski

“…The findings underscore the importance of lightweight, practical models and the integration of streamlined training processes in developing effective anomaly detection systems. …”
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IT diagnostics of Parkinson's disease based on voice markers and decreased motor activity by U. V. Vishniakou, X. Yiwei

“…The objectives of the article to propose the method for complex recognition of Parkinson's disease using machine learning, based on markers of voice analysis and changes in patient movements on known data sets. …”
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Artificial Intelligence-Driven Innovations in Oncology Drug Discovery: Transforming Traditional Pipelines and Enhancing Drug Design by Albani FG, Alghamdi SS, Almutairi MM, Alqahtani T

“…Fatimah G Albani,1,2 Sahar S Alghamdi,2– 4 Mohammed M Almutairi,5 Tariq Alqahtani2– 4 1Department of Biology, Faculty of Science, Princess Nourah bint Abdulrahman University, Al-Riyadh, Saudi Arabia; 2Medical Research Core Facility and Platforms, King Abdullah International Medical Research Center (KAIMRC), Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia; 3College of Pharmacy (COP), King Saud bin Abdulaziz University for Health Sciences (KSAU-HS), Riyadh, Kingdom of Saudi Arabia; 4King Abdulaziz Medical City, Ministry of the National Guard - Health Affairs, Riyadh, 11426, Saudi Arabia; 5Pharmacology and Toxicology Department, College of Pharmacy, King Saud University, Riyadh, Kingdom of Saudi ArabiaCorrespondence: Tariq Alqahtani, Email qahtanita@ksau-hs.edu.saAbstract: The integration of artificial intelligence (AI) into oncology drug discovery is redefining the traditional pipeline by accelerating discovery, optimizing drug efficacy, and minimizing toxicity. AI has enabled groundbreaking advancements in molecular modeling, simulation techniques, and the identification of novel compounds, including anti-tumor and antibodies, while elucidating mechanisms of drug toxicity. …”
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